This document explores the DL2_preprocessed dataset to gain an initial understanding of the data and investigate patterns in deliberate ignorance (DI) across scenarios, motives, and age groups.
dat <- read_csv("dl2_data_processed.csv")
## Rows: 33930 Columns: 42
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): ResponseId, Age_group, Prolific_ID, Scenario_Code, Want_to_know, e...
## dbl (34): Age, Duration..in.seconds., expectation, valence, arousal, sadness...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Exclude participants with missing DI Ratings.
dat_short <- dat %>%
filter(!is.na(want_to_know_bin))
dat_short <- dat_short %>%
mutate(
Scenario_Category = case_when(
Scenario_Content %in% c(
"hiv_test", "gen_risk_alzheimer", "gen_risk_huntington",
"life_expect_huntington", "spread_metast_cancer", "risk_anaesthesia",
"nutritional_deficiencies", "calories_food", "sex_of_child"
) ~ "Health",
Scenario_Content %in% c(
"partner_cheating", "partner_prev_relationship",
"converts",
"friend_polit_opin", "friend_sex_life"
) ~ "Social",
Scenario_Content %in% c(
"colleague_salary", "bank_balance",
"altern_recent_purchase", "friend_finance_problems"
) ~ "Finances",
Scenario_Content %in% c(
"success_chance_new_comp", "gender_job_applicant", "teaching_eval"
) ~ "Career",
Scenario_Content %in% c(
"hunger_crisis", "war", "holocaust_movie",
"child_abuse", "homeless_ppl"
) ~ "Society",
Scenario_Content %in% c(
"Kardashian_home", "ending_GoT"
) ~ "Entertainment",
Scenario_Content %in% c(
"global_warming", "factory_farming"
) ~ "Environment",
)
)
motive_vars <- c("expectation","valence","arousal", "sadness","anger","fear",
"jealousy","envy","disgust","regret","feelinggood","relief",
"happiness","complexity","relevance","reliability","relationships",
"conflict","politeness","notlying","unsureresp.","exclusion",
"self.percept.","actiongeneral","actionnow","disadvantage",
"goalinterfer","socialobligat","legalobligat","surprise","fairness")
data_wide <- dat_short %>%
select(ResponseId, Age, Age_group, Scenario_Code, all_of(motive_vars)) %>%
# long -> wide
pivot_wider(
id_cols = c(ResponseId, Age, Age_group),
names_from = Scenario_Code,
values_from = all_of(motive_vars),
names_sep = "_"
)
nrow(data_wide)
## [1] 1090
data_wide %>%
dplyr::group_by(Age_group) %>%
dplyr::summarise(count = n())
## # A tibble: 3 × 2
## Age_group count
## <chr> <int>
## 1 older 543
## 2 younger 522
## 3 <NA> 25
range(data_wide$Age, na.rm = TRUE)
## [1] 20 70
hist(data_wide$Age,
main = "Age range",
xlab = "Age",
ylab = "Frequency",
col = "skyblue",
border = "white")
ratings_count_person <- dat_short %>%
dplyr::filter(want_to_know_bin %in% c(0, 1)) %>%
dplyr::group_by(ResponseId) %>%
dplyr::summarise(
n_valid_ratings = n(),
.groups = "drop"
)
rating_cols <- setdiff(names(data_wide), c("ResponseId", "Age_group"))
motives_count_person <- data_wide %>%
mutate(
n_ratings_filled = rowSums(
select(., all_of(rating_cols)) != -99 & !is.na(select(., all_of(rating_cols)))
)
) %>%
select(ResponseId, n_ratings_filled)
### Combine Both Counts
ratings_combined <- merge(
ratings_count_person,
motives_count_person,
by = "ResponseId"
)
ratings_combined <- ratings_combined %>%
mutate(avg_ratings_per_scenario = n_ratings_filled / n_valid_ratings)
mean(ratings_combined$avg_ratings_per_scenario, na.rm = TRUE)
## [1] 28.19067
dat_short <- dat_short %>%
mutate(Age_group = ifelse(is.na(Age_group), "unknown", Age_group))
scenario_participants <- dat_short %>%
dplyr::group_by(Scenario_Content, Age_group) %>%
dplyr::summarise(n_ratings = n(), .groups = "drop")
scenario_participants
## # A tibble: 81 × 3
## Scenario_Content Age_group n_ratings
## <chr> <chr> <int>
## 1 Kardashian_home older 60
## 2 Kardashian_home unknown 1
## 3 Kardashian_home younger 48
## 4 altern_recent_purchase older 50
## 5 altern_recent_purchase unknown 1
## 6 altern_recent_purchase younger 55
## 7 bank_balance older 55
## 8 bank_balance younger 53
## 9 calories_food older 54
## 10 calories_food unknown 1
## # ℹ 71 more rows
Responses Want to Know / Not Want to Know (DI)
overall_DI <- dat_short %>%
dplyr::filter(!is.na(want_to_know_bin)) %>%
dplyr::summarise(
total_responses = n(),
total_DI = sum(want_to_know_bin == 1),
proportion_DI = mean(want_to_know_bin == 1)
)
overall_DI
## # A tibble: 1 × 3
## total_responses total_DI proportion_DI
## <int> <int> <dbl>
## 1 3233 950 0.294
scenario_counts <- dat_short %>%
dplyr::group_by(Scenario_Content) %>%
dplyr::summarise(n_ratings = n(), .groups = "drop")
scenario_counts
## # A tibble: 30 × 2
## Scenario_Content n_ratings
## <chr> <int>
## 1 Kardashian_home 109
## 2 altern_recent_purchase 106
## 3 bank_balance 108
## 4 calories_food 106
## 5 child_abuse 109
## 6 colleague_salary 103
## 7 converts 104
## 8 ending_GoT 107
## 9 factory_farming 106
## 10 friend_finance_problems 105
## # ℹ 20 more rows
di_scenario <- dat_short %>%
dplyr::group_by(Scenario_Content, want_to_know_bin) %>%
dplyr::summarise(n_ratings = n(),.groups = "drop")
di_scenario_full <- dat_short %>%
dplyr::group_by(Scenario_Content) %>%
dplyr::summarise(
n_0 = sum(want_to_know_bin == 0, na.rm = TRUE),
n_di = sum(want_to_know_bin == 1, na.rm = TRUE),
total = n_0 + n_di,
prop_1 = n_di / total,
.groups = "drop"
)%>%
dplyr::arrange(desc(prop_1))
di_scenario_full <- di_scenario_full %>%
mutate(Scenario_Content = factor(Scenario_Content, levels = Scenario_Content[order(prop_1)]))
ggplot(di_scenario_full, aes(x = Scenario_Content, y = prop_1)) +
geom_col(fill = "#E69F00") +
coord_flip() +
labs(
title = "Proportion of DI per Scenario",
x = "Scenario",
y = "Proportion of DI to total"
) +
theme_minimal(base_size = 14)
di_category <- dat_short %>%
dplyr::group_by(Scenario_Category, want_to_know_bin) %>%
dplyr::summarise(n_ratings = n(),.groups = "drop")
ggplot(di_category, aes(x = Scenario_Category, y = n_ratings, fill = factor(want_to_know_bin))) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = c("0" = "skyblue", "1" = "#E69F00"),
name = "Want to know?",
labels = c("0 = want to know", "1 = do NOT want to know (DI)")) +
labs(
title = "Want-to-Know Responses by Category",
x = "Category",
y = "Number of Ratings"
) +
theme_minimal(base_size = 12) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold", hjust = 0.5)
)
di_category_full <- dat_short %>%
dplyr::group_by(Scenario_Category) %>%
dplyr::summarise(
n_0 = sum(want_to_know_bin == 0, na.rm = TRUE),
n_di = sum(want_to_know_bin == 1, na.rm = TRUE),
total = n_0 + n_di,
prop_1 = n_di / total,
.groups = "drop"
)%>%
dplyr::arrange(desc(prop_1))
di_category_full <- di_category_full %>%
mutate(Scenario_Category = factor(Scenario_Category, levels = Scenario_Category[order(prop_1)]))
ggplot(di_category_full, aes(x = Scenario_Category, y = prop_1)) +
geom_col(fill = "#E69F00") +
coord_flip() +
labs(
title = "Proportion of DI per Category",
x = "Category",
y = "Proportion of DI to total"
) +
theme_minimal(base_size = 14)
Decisions Want to Know / Not to Know (DI)
overall_DI_age <- dat_short %>%
dplyr::filter(!is.na(want_to_know_bin)) %>%
dplyr::group_by(Age_group) %>%
dplyr::summarise(
total_responses = n(),
total_DI = sum(want_to_know_bin == 1),
proportion_DI = mean(want_to_know_bin == 1),
.groups = "drop"
)
overall_DI_age
## # A tibble: 3 × 4
## Age_group total_responses total_DI proportion_DI
## <chr> <int> <int> <dbl>
## 1 older 1629 556 0.341
## 2 unknown 39 10 0.256
## 3 younger 1565 384 0.245
positive: more young participants chose “not want to know”
scenario_age_prop <- dat_short %>%
dplyr::group_by(Scenario_Content, Age_group) %>%
dplyr::summarise(
total_participants = n(),
n_want_to_know = sum(want_to_know_bin == 1, na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(prop_want_to_know = n_want_to_know / total_participants) %>%
dplyr::select(-n_want_to_know, -total_participants) %>%
pivot_wider(
names_from = Age_group,
values_from = prop_want_to_know,
values_fill = 0
) %>%
mutate(diff_prop_young_old = younger - older) %>%
arrange(desc(diff_prop_young_old))
scenario_age_prop
## # A tibble: 30 × 5
## Scenario_Content older unknown younger diff_prop_young_old
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 gender_job_applicant 0.472 0 0.571 0.0997
## 2 homeless_ppl 0.386 0 0.472 0.0857
## 3 nutritional_deficiencies 0 0 0.0769 0.0769
## 4 success_chance_new_comp 0.0377 0 0.109 0.0714
## 5 bank_balance 0.109 0 0.170 0.0607
## 6 converts 0.0577 0 0.0962 0.0385
## 7 spread_metast_cancer 0.0690 0 0.0980 0.0291
## 8 teaching_eval 0.0577 0 0.0833 0.0256
## 9 holocaust_movie 0.305 0.333 0.319 0.0141
## 10 ending_GoT 0.903 0 0.909 0.00587
## # ℹ 20 more rows
scenario_age_long <- scenario_age_prop %>%
pivot_longer(cols = c(younger, older),
names_to = "Age_group",
values_to = "prop_want_to_know") %>%
group_by(Scenario_Content) %>%
mutate(mean_prop = mean(prop_want_to_know, na.rm = TRUE)) %>%
ungroup()
ggplot(scenario_age_long,
aes(x = reorder(Scenario_Content, mean_prop),
y = prop_want_to_know,
fill = Age_group)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("younger" = "#4CAF50", "older" = "#2196F3"),
labels = c("older", "younger")) +
labs(
title = "DI ratings by scenario and age group",
x = "Scenario",
y = "Proportion DI",
fill = "Age group"
) +
coord_flip() +
theme_minimal(base_size = 13)
positive: more young participants chose “not want to know”
category_age_prop <- dat_short %>%
dplyr::group_by(Scenario_Category, Age_group) %>%
dplyr::summarise(
total_participants = n(),
n_want_to_know = sum(want_to_know_bin == 1, na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(prop_want_to_know = n_want_to_know / total_participants) %>%
dplyr::select(-n_want_to_know, -total_participants) %>%
pivot_wider(
names_from = Age_group,
values_from = prop_want_to_know,
values_fill = 0
) %>%
mutate(diff_prop_young_old = younger - older) %>%
arrange(desc(diff_prop_young_old))
category_age_prop
## # A tibble: 7 × 5
## Scenario_Category older unknown younger diff_prop_young_old
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Career 0.190 0 0.264 0.0743
## 2 Environment 0.230 0.5 0.194 -0.0362
## 3 Health 0.192 0.188 0.121 -0.0711
## 4 Society 0.330 0.143 0.244 -0.0853
## 5 Entertainment 0.902 0.5 0.804 -0.0973
## 6 Finances 0.422 0 0.319 -0.102
## 7 Social 0.445 0.571 0.221 -0.223
scenario_age_long_cat <- category_age_prop %>%
pivot_longer(cols = c(younger, older),
names_to = "Age_group",
values_to = "prop_want_to_know") %>%
group_by(Scenario_Category) %>%
mutate(mean_prop = mean(prop_want_to_know, na.rm = TRUE)) %>%
ungroup()
ggplot(scenario_age_long_cat,
aes(x = reorder(Scenario_Category, mean_prop),
y = prop_want_to_know,
fill = Age_group)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("older" = "#4CAF50", "younger" = "#2196F3"),
labels = c("older", "younger")) +
labs(
title = "DI ratings by category and age group",
x = "Category",
y = "Response Not want to know (DI)",
fill = "Age group"
) +
coord_flip() +
theme_minimal(base_size = 13)
overall_motives <- dat_short %>%
summarise(across(all_of(motive_vars), ~ mean(.x[.x != -99], na.rm = TRUE))) %>%
pivot_longer(cols = everything(), names_to = "motive", values_to = "mean_rating") %>%
arrange(desc(mean_rating))
overall_motives
## # A tibble: 31 × 2
## motive mean_rating
## <chr> <dbl>
## 1 reliability 4.21
## 2 actionnow 3.87
## 3 relevance 3.84
## 4 actiongeneral 3.77
## 5 socialobligat 3.34
## 6 fear 3.33
## 7 arousal 3.28
## 8 valence 3.21
## 9 unsureresp. 3.16
## 10 self.percept. 3.11
## # ℹ 21 more rows
ratings_per_motive_mean <- dat_short %>%
dplyr::group_by(Scenario_Content) %>%
dplyr::summarise(
across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "mean_{.col}"
),
.groups = "drop"
)
ratings_long <- ratings_per_motive_mean %>%
pivot_longer(
cols = starts_with("mean_"),
names_to = "Motive",
values_to = "MeanRating"
)
ggplot(ratings_long, aes(x = Motive, y = Scenario_Content, fill = MeanRating)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(
title = "Mean Ratings of Motives per Scenario",
x = "Motive",
y = "Scenario",
fill = "Mean Rating"
) +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
top3_motives <- dat_short %>%
dplyr::group_by(Scenario_Content) %>%
dplyr::summarise(across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop") %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Content) %>%
slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
arrange(Scenario_Content, desc(avg_rating)) %>%
ungroup()
top3_motives_table <- dat_short %>%
dplyr::group_by(Scenario_Content) %>%
dplyr::summarise(
across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"
),
.groups = "drop"
) %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Content) %>%
slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
arrange(Scenario_Content, desc(avg_rating)) %>%
mutate(rank = row_number()) %>%
pivot_wider(
names_from = rank,
values_from = c(motive, avg_rating),
names_glue = "Top{rank}_{.value}"
) %>%
arrange(Scenario_Content)
knitr::kable(top3_motives_table, digits = 2, caption = "Top 3 Motives (Mean Ratings) per Scenario")
| Scenario_Content | Top1_motive | Top2_motive | Top3_motive | Top1_avg_rating | Top2_avg_rating | Top3_avg_rating |
|---|---|---|---|---|---|---|
| Kardashian_home | relevance | disgust | reliability | 2.43 | 2.31 | 2.21 |
| altern_recent_purchase | actionnow | regret | relevance | 3.96 | 3.76 | 3.60 |
| bank_balance | actionnow | relevance | reliability | 4.67 | 4.62 | 4.54 |
| calories_food | actionnow | reliability | relevance | 4.71 | 4.63 | 4.33 |
| child_abuse | reliability | socialobligat | actiongeneral | 4.57 | 3.97 | 3.83 |
| colleague_salary | relevance | reliability | conflict | 4.20 | 3.92 | 3.91 |
| converts | reliability | legalobligat | socialobligat | 4.25 | 4.20 | 4.17 |
| ending_GoT | surprise | regret | arousal | 4.54 | 4.13 | 3.31 |
| factory_farming | reliability | disgust | socialobligat | 4.28 | 3.92 | 3.88 |
| friend_finance_problems | actiongeneral | unsureresp. | socialobligat | 3.70 | 3.62 | 3.50 |
| friend_polit_opin | relationships | conflict | politeness | 3.81 | 3.79 | 3.54 |
| friend_sex_life | unsureresp. | regret | relationships | 3.60 | 3.50 | 3.46 |
| gen_risk_alzheimer | relevance | reliability | actiongeneral | 4.93 | 4.61 | 4.42 |
| gen_risk_huntington | relevance | reliability | actiongeneral | 5.19 | 4.66 | 4.52 |
| gender_job_applicant | fairness | reliability | actionnow | 3.93 | 3.61 | 3.51 |
| global_warming | reliability | actionnow | socialobligat | 4.79 | 4.52 | 4.26 |
| hiv_test | relevance | reliability | actionnow | 5.06 | 4.99 | 4.70 |
| holocaust_movie | reliability | arousal | sadness | 4.24 | 4.04 | 3.81 |
| homeless_ppl | socialobligat | reliability | sadness | 3.92 | 3.72 | 3.50 |
| hunger_crisis | reliability | socialobligat | valence | 4.45 | 4.08 | 3.96 |
| life_expect_huntington | relevance | goalinterfer | reliability | 5.08 | 4.69 | 4.57 |
| nutritional_deficiencies | reliability | relevance | actionnow | 5.00 | 4.96 | 4.91 |
| partner_cheating | reliability | relevance | relationships | 4.91 | 4.50 | 4.29 |
| partner_prev_relationship | regret | relationships | reliability | 3.84 | 3.83 | 3.66 |
| risk_anaesthesia | relevance | reliability | actionnow | 4.78 | 4.56 | 4.54 |
| sex_of_child | reliability | feelinggood | happiness | 4.01 | 3.86 | 3.81 |
| spread_metast_cancer | relevance | actionnow | reliability | 4.99 | 4.94 | 4.83 |
| success_chance_new_comp | reliability | actionnow | relevance | 5.16 | 5.07 | 4.85 |
| teaching_eval | relevance | actionnow | actiongeneral | 5.01 | 4.83 | 4.63 |
| war | reliability | actionnow | anger | 4.47 | 3.51 | 3.38 |
ggplot(top3_motives, aes(x = motive, y = Scenario_Content, fill = avg_rating)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(
title = "Top 3 Motives by Scenario",
x = "Motive",
y = "Scenario",
fill = "Mean Rating"
) +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ratings_per_motive_mean_cat <- dat_short %>%
dplyr::group_by(Scenario_Category) %>%
dplyr::summarise(
across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "mean_{.col}"
),
.groups = "drop"
)
ratings_per_motive_mean_cat
## # A tibble: 7 × 32
## Scenario_Category mean_expectation mean_valence mean_arousal mean_sadness
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Career 2.87 2.72 2.68 2.46
## 2 Entertainment 2.32 2.5 2.6 2.29
## 3 Environment 3.07 3.23 3.21 3.12
## 4 Finances 2.87 3.25 2.85 2.95
## 5 Health 3.34 3.32 3.55 3.23
## 6 Social 3.23 3.20 3.45 3.10
## 7 Society 3.16 3.50 3.57 3.60
## # ℹ 27 more variables: mean_anger <dbl>, mean_fear <dbl>, mean_jealousy <dbl>,
## # mean_envy <dbl>, mean_disgust <dbl>, mean_regret <dbl>,
## # mean_feelinggood <dbl>, mean_relief <dbl>, mean_happiness <dbl>,
## # mean_complexity <dbl>, mean_relevance <dbl>, mean_reliability <dbl>,
## # mean_relationships <dbl>, mean_conflict <dbl>, mean_politeness <dbl>,
## # mean_notlying <dbl>, mean_unsureresp. <dbl>, mean_exclusion <dbl>,
## # mean_self.percept. <dbl>, mean_actiongeneral <dbl>, mean_actionnow <dbl>, …
ratings_long_cat <- ratings_per_motive_mean_cat %>%
pivot_longer(
cols = starts_with("mean_"),
names_to = "Motive",
values_to = "MeanRating"
)
ggplot(ratings_long_cat, aes(x = Motive, y = Scenario_Category, fill = MeanRating)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(
title = "Mean Ratings of Motives by Category",
x = "Motive",
y = "Category",
fill = "Mean Rating"
) +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
top3_motives_cat <- dat_short %>%
dplyr::group_by(Scenario_Category) %>%
dplyr::summarise(across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop") %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Category) %>%
slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
arrange(Scenario_Category, desc(avg_rating)) %>%
ungroup()
top3_motives_cat_table <- dat_short %>%
dplyr::group_by(Scenario_Category) %>%
dplyr::summarise(
across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"
),
.groups = "drop"
) %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Category) %>%
slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
arrange(Scenario_Category, desc(avg_rating)) %>%
mutate(rank = row_number()) %>%
pivot_wider(
names_from = rank,
values_from = c(motive, avg_rating),
names_glue = "Top{rank}_{.value}"
) %>%
arrange(Scenario_Category)
knitr::kable(top3_motives_cat_table, digits = 2, caption = "Top 3 Motives (Mean Ratings) per Category")
| Scenario_Category | Top1_motive | Top2_motive | Top3_motive | Top1_avg_rating | Top2_avg_rating | Top3_avg_rating |
|---|---|---|---|---|---|---|
| Career | actionnow | reliability | actiongeneral | 4.48 | 4.42 | 4.22 |
| Entertainment | surprise | regret | reliability | 3.30 | 3.05 | 2.73 |
| Environment | reliability | actionnow | socialobligat | 4.54 | 4.11 | 4.08 |
| Finances | reliability | actionnow | actiongeneral | 3.88 | 3.80 | 3.76 |
| Health | relevance | reliability | actionnow | 4.79 | 4.65 | 4.52 |
| Social | relationships | reliability | conflict | 3.85 | 3.85 | 3.71 |
| Society | reliability | socialobligat | sadness | 4.29 | 3.68 | 3.60 |
ggplot(top3_motives_cat, aes(x = motive, y = Scenario_Category, fill = avg_rating)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(
title = "Top 3 Motives by Category",
x = "Motive",
y = "Category",
fill = "Mean Rating"
) +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
overall_motives_by_age <- dat_short %>%
dplyr::filter(Age_group %in% c("younger", "older")) %>%
dplyr::group_by(Age_group) %>%
dplyr::summarise(
across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}")
) %>%
tidyr::pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "mean_rating"
) %>%
dplyr::mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::arrange(Age_group, desc(mean_rating))
overall_motives_by_age
## # A tibble: 62 × 3
## Age_group motive mean_rating
## <chr> <chr> <dbl>
## 1 older reliability 4.39
## 2 older actionnow 3.83
## 3 older actiongeneral 3.78
## 4 older relevance 3.73
## 5 older socialobligat 3.26
## 6 older fear 3.24
## 7 older regret 3.16
## 8 older valence 3.15
## 9 older unsureresp. 3.14
## 10 older arousal 3.13
## # ℹ 52 more rows
ggplot(overall_motives_by_age, aes(x = reorder(motive, mean_rating),
y = mean_rating,
fill = Age_group)) +
geom_col(position = position_dodge(width = 0.8)) +
coord_flip() +
scale_fill_manual(values = c("older" = "#4CAF50", "younger" = "#2196F3"),
labels = c("older", "younger")) +
labs(
title = "Overall Motive Ratings by Age Group",
x = "Motive",
y = "Average Rating",
fill = "Age Group"
) +
theme_minimal(base_size = 12)
top3_motives_by_age <- dat_short %>%
dplyr::filter(Age_group %in% c("younger", "older")) %>%
dplyr::group_by(Scenario_Content, Age_group) %>%
dplyr::summarise(
across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop"
) %>%
tidyr::pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
dplyr::mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Content, Age_group) %>%
dplyr::slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
dplyr::arrange(Scenario_Content, Age_group, desc(avg_rating)) %>%
dplyr::ungroup()
top3_motives_by_age
## # A tibble: 180 × 4
## Scenario_Content Age_group motive avg_rating
## <chr> <chr> <chr> <dbl>
## 1 Kardashian_home older reliability 2.08
## 2 Kardashian_home older disgust 2
## 3 Kardashian_home older anger 1.77
## 4 Kardashian_home younger relevance 3.24
## 5 Kardashian_home younger self.percept. 2.89
## 6 Kardashian_home younger envy 2.83
## 7 altern_recent_purchase older regret 3.82
## 8 altern_recent_purchase older actionnow 3.72
## 9 altern_recent_purchase older reliability 3.69
## 10 altern_recent_purchase younger actionnow 4.15
## # ℹ 170 more rows
top3_motives_by_age_cat <- dat_short %>%
dplyr::filter(Age_group %in% c("younger", "older")) %>%
dplyr::group_by(Scenario_Category, Age_group) %>%
dplyr::summarise(
across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop"
) %>%
tidyr::pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
dplyr::mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(Scenario_Category, Age_group) %>%
dplyr::slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
dplyr::arrange(Scenario_Category, Age_group, desc(avg_rating)) %>%
dplyr::ungroup()
top3_motives_by_age_cat
## # A tibble: 42 × 4
## Scenario_Category Age_group motive avg_rating
## <chr> <chr> <chr> <dbl>
## 1 Career older reliability 4.77
## 2 Career older actionnow 4.52
## 3 Career older actiongeneral 4.46
## 4 Career younger actionnow 4.44
## 5 Career younger relevance 4.21
## 6 Career younger reliability 4.05
## 7 Entertainment older surprise 3.06
## 8 Entertainment older reliability 2.79
## 9 Entertainment older regret 2.79
## 10 Entertainment younger surprise 3.62
## # ℹ 32 more rows
ggplot(top3_motives_by_age_cat,
aes(x = reorder(motive, avg_rating), y = avg_rating, fill = Age_group)) +
geom_col(position = "dodge") +
facet_wrap(~ Scenario_Category, scales = "free_x", ncol = 5) +
coord_flip() +
scale_fill_manual(values = c("older" = "#4CAF50", "younger" = "#2196F3"),
labels = c("older", "younger")) +
labs(
title = "Top 3 Motives by Scenario Category and Age Group",
x = "Motive",
y = "Mean Rating",
fill = "Age Group"
) +
theme_minimal(base_size = 12)
ratings_per_motive_DI <- dat_short %>%
dplyr::group_by(want_to_know_bin) %>%
dplyr::summarise(
across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "mean_{.col}"
),
.groups = "drop"
)
Highly rated motives where participants gave response “not want to know” (DI)
motive_means_di <- dat_short %>%
filter(want_to_know_bin == 1) %>%
group_by(Scenario_Content) %>%
summarise(across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"
)) %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "mean_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
arrange(Scenario_Content, desc(mean_rating))
scenario_props <- dat_short %>%
group_by(Scenario_Content) %>%
summarise(
prop_want_1 = mean(want_to_know_bin == 1, na.rm = TRUE),
.groups = "drop"
)
motive_means_di <- motive_means_di %>%
left_join(scenario_props, by = "Scenario_Content")
motive_means_di
## # A tibble: 930 × 4
## Scenario_Content motive mean_rating prop_want_1
## <chr> <chr> <dbl> <dbl>
## 1 Kardashian_home relevance 2.38 0.817
## 2 Kardashian_home disgust 2.31 0.817
## 3 Kardashian_home actionnow 2.05 0.817
## 4 Kardashian_home anger 2.05 0.817
## 5 Kardashian_home envy 1.9 0.817
## 6 Kardashian_home valence 1.89 0.817
## 7 Kardashian_home reliability 1.88 0.817
## 8 Kardashian_home self.percept. 1.83 0.817
## 9 Kardashian_home jealousy 1.78 0.817
## 10 Kardashian_home regret 1.68 0.817
## # ℹ 920 more rows
Highly rated motives where participants gave response “want to know”
motive_means_noDI <- dat_short %>%
filter(want_to_know_bin == 0) %>%
group_by(Scenario_Content) %>%
summarise(across(
all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"
)) %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "mean_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
arrange(Scenario_Content, desc(mean_rating))
motive_means_noDI <- motive_means_noDI %>%
left_join(scenario_props, by = "Scenario_Content")
motive_means_noDI
## # A tibble: 930 × 4
## Scenario_Content motive mean_rating prop_want_1
## <chr> <chr> <dbl> <dbl>
## 1 Kardashian_home reliability 3.53 0.817
## 2 Kardashian_home surprise 3.37 0.817
## 3 Kardashian_home envy 3.3 0.817
## 4 Kardashian_home feelinggood 3.2 0.817
## 5 Kardashian_home happiness 3.05 0.817
## 6 Kardashian_home self.percept. 3 0.817
## 7 Kardashian_home arousal 2.67 0.817
## 8 Kardashian_home politeness 2.67 0.817
## 9 Kardashian_home relevance 2.65 0.817
## 10 Kardashian_home valence 2.63 0.817
## # ℹ 920 more rows
ratings_long_DI <- ratings_per_motive_DI %>%
pivot_longer(
cols = starts_with("mean_"),
names_to = "motive",
values_to = "mean_rating"
) %>%
mutate(
motive = sub("^mean_", "", motive),
want_to_know_bin = factor(
want_to_know_bin,
levels = c(1, 0),
labels = c("Not want to know (DI)", "Want to know")
)
)
ggplot(ratings_long_DI, aes(x = motive, y = mean_rating, group = want_to_know_bin, color = want_to_know_bin)) +
geom_line(linewidth = 1) +
geom_point(size = 2) +
scale_color_manual(
values = c(
"Want to know" = "#56B4E9" ,
"Not want to know (DI)" = "#E69F00"
)
) +
theme_minimal(base_size = 12) +
labs(
title = "Mean Motive Ratings by DI Response",
x = "Motive",
y = "Mean Rating",
color = "Response"
) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold", size = 14)
)
top3_motives_DI <- dat_short %>%
dplyr::group_by(want_to_know_bin) %>%
dplyr::summarise(across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop") %>%
pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(want_to_know_bin) %>%
slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
arrange(want_to_know_bin, desc(avg_rating)) %>%
ungroup()
top3_motives_DI
## # A tibble: 6 × 3
## want_to_know_bin motive avg_rating
## <dbl> <chr> <dbl>
## 1 0 reliability 4.57
## 2 0 actionnow 4.26
## 3 0 relevance 4.19
## 4 1 regret 3.66
## 5 1 valence 3.37
## 6 1 reliability 3.27
top3_motives_by_age_DI <- dat_short %>%
dplyr::filter(Age_group %in% c("younger", "older")) %>%
dplyr::group_by(want_to_know_bin, Age_group) %>%
dplyr::summarise(
across(all_of(motive_vars),
~ mean(.x[.x != -99], na.rm = TRUE),
.names = "avg_{.col}"),
.groups = "drop"
) %>%
tidyr::pivot_longer(
cols = starts_with("avg_"),
names_to = "motive",
values_to = "avg_rating"
) %>%
dplyr::mutate(motive = sub("^avg_", "", motive)) %>%
dplyr::group_by(want_to_know_bin, Age_group) %>%
dplyr::slice_max(avg_rating, n = 3, with_ties = FALSE) %>%
dplyr::arrange(want_to_know_bin, Age_group, desc(avg_rating)) %>%
dplyr::ungroup()
top3_motives_by_age_DI
## # A tibble: 12 × 4
## want_to_know_bin Age_group motive avg_rating
## <dbl> <chr> <chr> <dbl>
## 1 0 older reliability 4.87
## 2 0 older actionnow 4.38
## 3 0 older actiongeneral 4.28
## 4 0 younger reliability 4.30
## 5 0 younger actionnow 4.16
## 6 0 younger relevance 4.15
## 7 1 older regret 3.60
## 8 1 older reliability 3.43
## 9 1 older valence 3.22
## 10 1 younger regret 3.74
## 11 1 younger valence 3.58
## 12 1 younger sadness 3.46
ggplot(top3_motives_by_age_DI,
aes(x = reorder(motive, avg_rating), y = avg_rating, fill = Age_group)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
facet_wrap(~ want_to_know_bin, scales = "free_x", ncol = 2,
labeller = labeller(want_to_know_bin = c(`1` = "Do not want to know (DI)", `0` = "Want to know"))) +
scale_fill_manual(values = c("older" = "#4CAF50", "younger" = "#2196F3"),
labels = c("older", "younger")) +
labs(
title = "Top 3 Motives by Age Group and DI Response",
x = "Motive",
y = "Average Rating",
fill = "Age Group"
) +
theme_minimal(base_size = 12)
dat_cor_expect_val <- dat_short %>%
filter(Scenario_Content=="partner_prev_relationship" & expectation!=-99 & valence!=-99)
cor(
dat_cor_expect_val$expectation,
dat_cor_expect_val$valence
)
## [1] 0.5405306
dat_cor_sad_ang<-dat_short%>%filter(Scenario_Content=="partner_prev_relationship" & sadness!=-99 & anger !=-99)
cor(
dat_cor_sad_ang$sadness,
dat_cor_sad_ang$anger
)
## [1] 0.7297495
corr_scenario <- dat_short %>%
group_by(Scenario_Content) %>%
summarise(
cor_matrix = list(
cor(
mutate(across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "partner_prev_relationship")]]
## expectation valence arousal sadness anger fear
## expectation 1.0000000 0.54053056 0.5595805 0.6244643 0.4200598 0.6014518
## valence 0.5405306 1.00000000 0.6877440 0.7036417 0.7315410 0.6579973
## arousal 0.5595805 0.68774397 1.0000000 0.7054604 0.7015077 0.6932262
## sadness 0.6244643 0.70364174 0.7054604 1.0000000 0.7297495 0.6943635
## anger 0.4200598 0.73154102 0.7015077 0.7297495 1.0000000 0.5776163
## fear 0.6014518 0.65799731 0.6932262 0.6943635 0.5776163 1.0000000
## jealousy 0.5571235 0.67675471 0.5701327 0.5894520 0.5708461 0.6190836
## envy 0.4306493 0.52328698 0.4245787 0.4276729 0.4897417 0.5094963
## disgust 0.4895966 0.51729075 0.5406767 0.4875126 0.5982754 0.4902458
## regret 0.5721047 0.65603234 0.6849295 0.6046526 0.5774978 0.6565768
## feelinggood 0.4646819 0.34949899 0.4073026 0.4046300 0.4368188 0.4471651
## relief 0.4494211 0.42946558 0.4922044 0.4845223 0.3545884 0.5189305
## happiness 0.4085344 0.37503800 0.4425206 0.4528634 0.4663663 0.5227876
## complexity 0.2560937 0.42100858 0.3755808 0.4063908 0.2989286 0.5606756
## relevance 0.4112834 0.21048994 0.3289167 0.3224455 0.2446929 0.3517321
## reliability 0.1682655 0.25777812 0.1030618 0.1640056 0.1546212 0.2959762
## relationships 0.5519030 0.58520618 0.6487949 0.5760986 0.4556461 0.6341877
## conflict 0.5455372 0.64417707 0.6407215 0.6445533 0.6093191 0.5838760
## politeness 0.2255147 0.08158145 0.1616687 0.1781042 0.1466799 0.1865269
## notlying 0.2565170 0.36811938 0.4154499 0.4222965 0.5208587 0.4199776
## unsureresp. 0.5442334 0.54100128 0.5913108 0.5916362 0.4391878 0.6385927
## exclusion 0.3757505 0.37940209 0.3547720 0.4803731 0.3522943 0.5663411
## self.percept. 0.4825401 0.51770828 0.4956342 0.5836077 0.5540944 0.4919540
## actiongeneral 0.2411682 0.32147998 0.3339778 0.3136727 0.3151412 0.4691348
## actionnow 0.2490104 0.22099676 0.2276929 0.3275498 0.2990095 0.4259542
## disadvantage 0.5150366 0.54520743 0.5129606 0.5748098 0.4819757 0.6467611
## goalinterfer 0.3500015 0.42698815 0.4901830 0.4194161 0.3986879 0.6332990
## socialobligat 0.1663572 0.22683702 0.2747528 0.2241221 0.2396766 0.2819600
## legalobligat 0.2217388 0.29087230 0.2941887 0.2556700 0.3088746 0.3801338
## surprise 0.1841026 0.30305333 0.3469458 0.2472141 0.3599058 0.2721208
## fairness 0.4405053 0.42451998 0.4486068 0.4498880 0.3870228 0.4191943
## jealousy envy disgust regret feelinggood relief
## expectation 0.5571235 0.4306493 0.4895966 0.57210473 0.4646819 0.4494211
## valence 0.6767547 0.5232870 0.5172908 0.65603234 0.3494990 0.4294656
## arousal 0.5701327 0.4245787 0.5406767 0.68492948 0.4073026 0.4922044
## sadness 0.5894520 0.4276729 0.4875126 0.60465258 0.4046300 0.4845223
## anger 0.5708461 0.4897417 0.5982754 0.57749775 0.4368188 0.3545884
## fear 0.6190836 0.5094963 0.4902458 0.65657679 0.4471651 0.5189305
## jealousy 1.0000000 0.7764850 0.4861889 0.49099503 0.4349026 0.4122725
## envy 0.7764850 1.0000000 0.5080078 0.43915629 0.4234701 0.3556001
## disgust 0.4861889 0.5080078 1.0000000 0.57334616 0.4305344 0.4728058
## regret 0.4909950 0.4391563 0.5733462 1.00000000 0.3365803 0.3491648
## feelinggood 0.4349026 0.4234701 0.4305344 0.33658033 1.0000000 0.5619559
## relief 0.4122725 0.3556001 0.4728058 0.34916476 0.5619559 1.0000000
## happiness 0.3957612 0.3394258 0.4273881 0.38740129 0.7518428 0.5829304
## complexity 0.3042932 0.2890438 0.3868763 0.34419413 0.2816479 0.3327647
## relevance 0.3213799 0.3332719 0.3627570 0.18335833 0.3478829 0.4666935
## reliability 0.2378202 0.1913660 0.1465199 0.07836074 0.2646387 0.3175479
## relationships 0.4639859 0.3103528 0.4685237 0.61739372 0.2913850 0.4429336
## conflict 0.4554408 0.3327501 0.5288821 0.71078136 0.2195237 0.3775870
## politeness 0.2026887 0.2875136 0.2585464 0.15496756 0.3844545 0.3489122
## notlying 0.3364768 0.2908880 0.4186209 0.33613106 0.3401327 0.2790404
## unsureresp. 0.5428649 0.4196665 0.3986932 0.62464095 0.3117996 0.4263320
## exclusion 0.4188946 0.3370483 0.4665212 0.33833889 0.3610546 0.4601092
## self.percept. 0.6194980 0.5452117 0.4813286 0.41377375 0.4196568 0.4836433
## actiongeneral 0.3124495 0.2858185 0.3174594 0.19444226 0.4073763 0.4696599
## actionnow 0.2657821 0.2703720 0.3801810 0.18693119 0.4503749 0.4168650
## disadvantage 0.5004436 0.3948400 0.3973548 0.41880719 0.3911815 0.4585804
## goalinterfer 0.3707545 0.3846519 0.4579401 0.37499611 0.4547734 0.5034747
## socialobligat 0.2752694 0.3360647 0.3477241 0.17879460 0.2985591 0.3272595
## legalobligat 0.1507504 0.2470298 0.3661389 0.23343780 0.4401936 0.4127753
## surprise 0.3172123 0.2418113 0.3786073 0.25429297 0.3215005 0.2786199
## fairness 0.3767349 0.2636543 0.2967229 0.34995888 0.4259879 0.4284807
## happiness complexity relevance reliability relationships
## expectation 0.4085344 0.2560937 0.41128341 0.16826554 0.5519030
## valence 0.3750380 0.4210086 0.21048994 0.25777812 0.5852062
## arousal 0.4425206 0.3755808 0.32891674 0.10306183 0.6487949
## sadness 0.4528634 0.4063908 0.32244545 0.16400557 0.5760986
## anger 0.4663663 0.2989286 0.24469290 0.15462123 0.4556461
## fear 0.5227876 0.5606756 0.35173210 0.29597622 0.6341877
## jealousy 0.3957612 0.3042932 0.32137986 0.23782020 0.4639859
## envy 0.3394258 0.2890438 0.33327187 0.19136595 0.3103528
## disgust 0.4273881 0.3868763 0.36275698 0.14651992 0.4685237
## regret 0.3874013 0.3441941 0.18335833 0.07836074 0.6173937
## feelinggood 0.7518428 0.2816479 0.34788295 0.26463866 0.2913850
## relief 0.5829304 0.3327647 0.46669346 0.31754786 0.4429336
## happiness 1.0000000 0.3003727 0.35067026 0.36814533 0.3788644
## complexity 0.3003727 1.0000000 0.12724709 0.23493631 0.3991169
## relevance 0.3506703 0.1272471 1.00000000 0.27173573 0.2989484
## reliability 0.3681453 0.2349363 0.27173573 1.00000000 0.1766088
## relationships 0.3788644 0.3991169 0.29894839 0.17660875 1.0000000
## conflict 0.3007044 0.3008869 0.21943898 0.09223134 0.7164144
## politeness 0.2132055 0.3178124 0.26405952 0.17726943 0.2477064
## notlying 0.2656731 0.4354978 0.14982985 0.17227466 0.3578295
## unsureresp. 0.2829148 0.4290835 0.18106842 0.12327120 0.6340452
## exclusion 0.3888697 0.6412744 0.23889137 0.26793838 0.4754813
## self.percept. 0.4805415 0.3262038 0.46778221 0.18555314 0.4996641
## actiongeneral 0.3791194 0.3642364 0.51532940 0.41052696 0.3334921
## actionnow 0.3820425 0.2727468 0.52409668 0.33063214 0.1655913
## disadvantage 0.4179869 0.4727817 0.21541649 0.19863059 0.5008206
## goalinterfer 0.4932406 0.6507618 0.34699259 0.21434757 0.4918325
## socialobligat 0.2588801 0.3984926 0.22719273 0.02140207 0.2954058
## legalobligat 0.4063236 0.4539661 0.27898906 0.14628261 0.2720738
## surprise 0.2690900 0.4449465 0.09185564 0.14611227 0.3105406
## fairness 0.2742252 0.2277163 0.29086021 0.20278852 0.4596458
## conflict politeness notlying unsureresp. exclusion
## expectation 0.54553723 0.22551469 0.2565170 0.5442334 0.3757505
## valence 0.64417707 0.08158145 0.3681194 0.5410013 0.3794021
## arousal 0.64072145 0.16166866 0.4154499 0.5913108 0.3547720
## sadness 0.64455326 0.17810415 0.4222965 0.5916362 0.4803731
## anger 0.60931909 0.14667994 0.5208587 0.4391878 0.3522943
## fear 0.58387600 0.18652695 0.4199776 0.6385927 0.5663411
## jealousy 0.45544079 0.20268873 0.3364768 0.5428649 0.4188946
## envy 0.33275008 0.28751361 0.2908880 0.4196665 0.3370483
## disgust 0.52888206 0.25854635 0.4186209 0.3986932 0.4665212
## regret 0.71078136 0.15496756 0.3361311 0.6246410 0.3383389
## feelinggood 0.21952372 0.38445453 0.3401327 0.3117996 0.3610546
## relief 0.37758695 0.34891220 0.2790404 0.4263320 0.4601092
## happiness 0.30070436 0.21320551 0.2656731 0.2829148 0.3888697
## complexity 0.30088694 0.31781242 0.4354978 0.4290835 0.6412744
## relevance 0.21943898 0.26405952 0.1498299 0.1810684 0.2388914
## reliability 0.09223134 0.17726943 0.1722747 0.1232712 0.2679384
## relationships 0.71641438 0.24770640 0.3578295 0.6340452 0.4754813
## conflict 1.00000000 0.20157160 0.3636187 0.5661560 0.3774948
## politeness 0.20157160 1.00000000 0.3804101 0.2232360 0.4091917
## notlying 0.36361873 0.38041013 1.0000000 0.3722002 0.5296668
## unsureresp. 0.56615601 0.22323596 0.3722002 1.0000000 0.3793391
## exclusion 0.37749481 0.40919170 0.5296668 0.3793391 1.0000000
## self.percept. 0.44376619 0.39518543 0.3359094 0.4608880 0.5275708
## actiongeneral 0.21538717 0.26622167 0.4688308 0.2401251 0.4826288
## actionnow 0.14890415 0.22975073 0.2475926 0.1691725 0.3924125
## disadvantage 0.36615178 0.24592480 0.5126067 0.4897496 0.5718544
## goalinterfer 0.36490271 0.35586659 0.4456845 0.4564156 0.6331446
## socialobligat 0.24674410 0.49924473 0.5241934 0.1978420 0.4949087
## legalobligat 0.28561001 0.51050918 0.5098555 0.1954498 0.6258379
## surprise 0.29801468 0.28175122 0.4566503 0.3202888 0.4569351
## fairness 0.41782193 0.36612866 0.5420908 0.3371094 0.4437252
## self.percept. actiongeneral actionnow disadvantage goalinterfer
## expectation 0.4825401 0.2411682 0.2490104 0.5150366 0.3500015
## valence 0.5177083 0.3214800 0.2209968 0.5452074 0.4269881
## arousal 0.4956342 0.3339778 0.2276929 0.5129606 0.4901830
## sadness 0.5836077 0.3136727 0.3275498 0.5748098 0.4194161
## anger 0.5540944 0.3151412 0.2990095 0.4819757 0.3986879
## fear 0.4919540 0.4691348 0.4259542 0.6467611 0.6332990
## jealousy 0.6194980 0.3124495 0.2657821 0.5004436 0.3707545
## envy 0.5452117 0.2858185 0.2703720 0.3948400 0.3846519
## disgust 0.4813286 0.3174594 0.3801810 0.3973548 0.4579401
## regret 0.4137737 0.1944423 0.1869312 0.4188072 0.3749961
## feelinggood 0.4196568 0.4073763 0.4503749 0.3911815 0.4547734
## relief 0.4836433 0.4696599 0.4168650 0.4585804 0.5034747
## happiness 0.4805415 0.3791194 0.3820425 0.4179869 0.4932406
## complexity 0.3262038 0.3642364 0.2727468 0.4727817 0.6507618
## relevance 0.4677822 0.5153294 0.5240967 0.2154165 0.3469926
## reliability 0.1855531 0.4105270 0.3306321 0.1986306 0.2143476
## relationships 0.4996641 0.3334921 0.1655913 0.5008206 0.4918325
## conflict 0.4437662 0.2153872 0.1489042 0.3661518 0.3649027
## politeness 0.3951854 0.2662217 0.2297507 0.2459248 0.3558666
## notlying 0.3359094 0.4688308 0.2475926 0.5126067 0.4456845
## unsureresp. 0.4608880 0.2401251 0.1691725 0.4897496 0.4564156
## exclusion 0.5275708 0.4826288 0.3924125 0.5718544 0.6331446
## self.percept. 1.0000000 0.3799831 0.3334828 0.5524089 0.4542851
## actiongeneral 0.3799831 1.0000000 0.6535328 0.3745729 0.4987703
## actionnow 0.3334828 0.6535328 1.0000000 0.2743954 0.3720016
## disadvantage 0.5524089 0.3745729 0.2743954 1.0000000 0.6248326
## goalinterfer 0.4542851 0.4987703 0.3720016 0.6248326 1.0000000
## socialobligat 0.4798507 0.4282064 0.3418547 0.4228362 0.4608952
## legalobligat 0.3431165 0.4864817 0.4382445 0.4457579 0.5904573
## surprise 0.4067980 0.2725631 0.2924018 0.2825278 0.3646355
## fairness 0.3730390 0.3478270 0.1886858 0.4623365 0.3019871
## socialobligat legalobligat surprise fairness
## expectation 0.16635716 0.2217388 0.18410256 0.4405053
## valence 0.22683702 0.2908723 0.30305333 0.4245200
## arousal 0.27475281 0.2941887 0.34694578 0.4486068
## sadness 0.22412211 0.2556700 0.24721410 0.4498880
## anger 0.23967656 0.3088746 0.35990575 0.3870228
## fear 0.28196004 0.3801338 0.27212081 0.4191943
## jealousy 0.27526943 0.1507504 0.31721232 0.3767349
## envy 0.33606471 0.2470298 0.24181129 0.2636543
## disgust 0.34772409 0.3661389 0.37860734 0.2967229
## regret 0.17879460 0.2334378 0.25429297 0.3499589
## feelinggood 0.29855907 0.4401936 0.32150051 0.4259879
## relief 0.32725952 0.4127753 0.27861988 0.4284807
## happiness 0.25888013 0.4063236 0.26909005 0.2742252
## complexity 0.39849262 0.4539661 0.44494650 0.2277163
## relevance 0.22719273 0.2789891 0.09185564 0.2908602
## reliability 0.02140207 0.1462826 0.14611227 0.2027885
## relationships 0.29540576 0.2720738 0.31054059 0.4596458
## conflict 0.24674410 0.2856100 0.29801468 0.4178219
## politeness 0.49924473 0.5105092 0.28175122 0.3661287
## notlying 0.52419338 0.5098555 0.45665026 0.5420908
## unsureresp. 0.19784197 0.1954498 0.32028885 0.3371094
## exclusion 0.49490872 0.6258379 0.45693511 0.4437252
## self.percept. 0.47985071 0.3431165 0.40679798 0.3730390
## actiongeneral 0.42820640 0.4864817 0.27256309 0.3478270
## actionnow 0.34185468 0.4382445 0.29240181 0.1886858
## disadvantage 0.42283625 0.4457579 0.28252778 0.4623365
## goalinterfer 0.46089524 0.5904573 0.36463546 0.3019871
## socialobligat 1.00000000 0.5761903 0.28121536 0.2794852
## legalobligat 0.57619031 1.0000000 0.39534405 0.4291377
## surprise 0.28121536 0.3953440 1.00000000 0.3644715
## fairness 0.27948524 0.4291377 0.36447148 1.0000000
unique(dat_short$Scenario_Content)
## [1] "hiv_test" "gen_risk_alzheimer"
## [3] "gen_risk_huntington" "life_expect_huntington"
## [5] "risk_anaesthesia" "nutritional_deficiencies"
## [7] "calories_food" "sex_of_child"
## [9] "partner_cheating" "partner_prev_relationship"
## [11] "converts" "friend_polit_opin"
## [13] "friend_sex_life" "colleague_salary"
## [15] "bank_balance" "altern_recent_purchase"
## [17] "friend_finance_problems" "teaching_eval"
## [19] "success_chance_new_comp" "gender_job_applicant"
## [21] "hunger_crisis" "war"
## [23] "holocaust_movie" "child_abuse"
## [25] "homeless_ppl" "Kardashian_home"
## [27] "ending_GoT" "global_warming"
## [29] "factory_farming" "spread_metast_cancer"
#correlation matrix for scenario
mat_hiv <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "hiv_test")]]
#long format
mat_long_hiv <- melt(mat_hiv, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_hiv, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Hiv Test")
strong_corr_hiv <- mat_long_hiv %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_alz <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "gen_risk_alzheimer")]]
#long format
mat_long_alz <- melt(mat_alz, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_alz, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Genetic Risk Alzheimer")
strong_corr_alz <- mat_long_alz %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_hunt_risk <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "gen_risk_huntington")]]
#long format
mat_long_hunt_risk <- melt(mat_hunt_risk, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_hunt_risk, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Genetic Risk Huntington")
strong_corr_hunt_risk <- mat_long_hunt_risk %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_hunt_life <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "life_expect_huntington")]]
#long format
mat_long_hunt_life <- melt(mat_hunt_life, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_hunt_life, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Life expectation Huntington")
strong_corr_hunt_life <- mat_long_hunt_life %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_got <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "ending_GoT")]]
#long format
mat_long_got <- melt(mat_got, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_got, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Ending GoT")
strong_corr_got <- mat_long_got %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_kardashian <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "Kardashian_home")]]
#long format
mat_long_kardashian <- melt(mat_kardashian, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_kardashian, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Kardashian Home")
strong_corr_kardashian <- mat_long_kardashian %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_purchase <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "altern_recent_purchase")]]
#long format
mat_long_purchase <- melt(mat_purchase, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_purchase, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Altern recent purchase ")
### strong correlations altern recent purchase
strong_corr_purchase <- mat_long_purchase %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_friend_sex <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "friend_sex_life")]]
#long format
mat_long_friend_sex <- melt(mat_friend_sex, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_friend_sex, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Friend Sex Life")
strong_corr_friend_sex <- mat_long_friend_sex %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_relationship <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "partner_prev_relationship")]]
#long format
mat_long_relationship <- melt(mat_relationship, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_relationship, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Partner Previous Relationship")
### strong correlations partner previous relationship
strong_corr_relationship <- mat_long_relationship %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for scenario
mat_child <- corr_scenario$cor_matrix[[which(corr_scenario$Scenario_Content == "sex_of_child")]]
#long format
mat_long_child <- melt(mat_child, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_child, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Sex of Child")
strong_corr_child <- mat_long_child %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
unique(dat_short$Scenario_Category)
## [1] "Health" "Social" "Finances" "Career"
## [5] "Society" "Entertainment" "Environment"
corr_category <- dat_short %>%
group_by(Scenario_Category) %>%
summarise(
cor_matrix = list(
{
temp <- across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x))
cor(as.data.frame(temp), use = "pairwise.complete.obs")
}
),
.groups = "drop"
)
#correlation matrix for category
mat_health <- corr_category$cor_matrix[[which(corr_category$Scenario_Category == "Health")]]
#long format
mat_long_health <- melt(mat_health, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_health, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Health")
strong_corr_health <- mat_long_health %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for category
mat_entertainment <- corr_category$cor_matrix[[which(corr_category$Scenario_Category == "Entertainment")]]
#long format
mat_long_entertainment <- melt(mat_entertainment, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_entertainment, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Entertainment")
strong_corr_entertainment <- mat_long_entertainment %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for category
mat_career <- corr_category$cor_matrix[[which(corr_category$Scenario_Category == "Career")]]
#long format
mat_long_career <- melt(mat_career, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_career, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Career")
strong_corr_career <- mat_long_career %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
#correlation matrix for category
mat_finances <- corr_category$cor_matrix[[which(corr_category$Scenario_Category == "Finances")]]
#long format
mat_long_finances <- melt(mat_finances, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_finances, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Finances")
strong_corr_finances <- mat_long_finances %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
corr_overall <- dat_short %>%
summarise(
cor_matrix = list(
cor(
mutate(across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
corr_overall$cor_matrix[[1]]
## expectation valence arousal sadness anger fear
## expectation 1.0000000 0.5200371 0.4275209 0.4980280 0.4294684 0.4961903
## valence 0.5200371 1.0000000 0.5856840 0.6980167 0.6277802 0.6260914
## arousal 0.4275209 0.5856840 1.0000000 0.6455394 0.5749443 0.5662476
## sadness 0.4980280 0.6980167 0.6455394 1.0000000 0.6560944 0.6001209
## anger 0.4294684 0.6277802 0.5749443 0.6560944 1.0000000 0.5171756
## fear 0.4961903 0.6260914 0.5662476 0.6001209 0.5171756 1.0000000
## jealousy 0.2773749 0.3732421 0.3316619 0.3591771 0.4222898 0.3505392
## envy 0.2248701 0.3398443 0.2838773 0.3299173 0.3721670 0.2942678
## disgust 0.3571635 0.4977043 0.4229284 0.4855905 0.5796880 0.3603988
## regret 0.4172377 0.5230210 0.4015322 0.4589669 0.4123120 0.4818549
## feelinggood 0.2330411 0.3233116 0.3369765 0.2699107 0.2362157 0.3302487
## relief 0.2987146 0.3450466 0.3481085 0.3054552 0.2758024 0.4424290
## happiness 0.2397001 0.3033323 0.3567941 0.2897181 0.2648376 0.3256109
## complexity 0.2775573 0.3595164 0.3589919 0.3708931 0.3563017 0.3933162
## relevance 0.2564659 0.2370984 0.2509937 0.2188205 0.1788983 0.3484988
## reliability 0.1854527 0.1823014 0.2046020 0.1811144 0.1793014 0.2501147
## relationships 0.3238377 0.3874311 0.3990074 0.3952525 0.3798968 0.4695966
## conflict 0.3057135 0.3896549 0.3566116 0.3804781 0.4176043 0.4136184
## politeness 0.1143631 0.1807060 0.2370163 0.2207393 0.2247762 0.1663450
## notlying 0.2037159 0.3077725 0.2709800 0.2821882 0.2834920 0.3135511
## unsureresp. 0.4284112 0.5009429 0.4699303 0.4915051 0.4171232 0.5186386
## exclusion 0.2237651 0.3107659 0.3245321 0.3181984 0.3315133 0.3677919
## self.percept. 0.3494316 0.4407834 0.4291907 0.4290053 0.4007583 0.4295134
## actiongeneral 0.2783822 0.2648632 0.2850313 0.2665760 0.2288970 0.3605213
## actionnow 0.2306814 0.1851908 0.2163829 0.1746061 0.1617760 0.2888031
## disadvantage 0.3483148 0.4164990 0.3594263 0.3543468 0.3638942 0.4692187
## goalinterfer 0.3181815 0.3870351 0.3287547 0.3658174 0.3141622 0.4715779
## socialobligat 0.2642331 0.2765725 0.3250004 0.2965516 0.2943342 0.3209490
## legalobligat 0.2114515 0.2467779 0.2701334 0.2388927 0.2741916 0.3217720
## surprise 0.2024977 0.2226430 0.3404616 0.2424084 0.2122025 0.2379468
## fairness 0.1707168 0.2487619 0.2622549 0.2522314 0.2632953 0.2557432
## jealousy envy disgust regret feelinggood relief
## expectation 0.27737492 0.22487008 0.35716352 0.41723774 0.2330411 0.2987146
## valence 0.37324214 0.33984434 0.49770430 0.52302102 0.3233116 0.3450466
## arousal 0.33166187 0.28387732 0.42292838 0.40153222 0.3369765 0.3481085
## sadness 0.35917706 0.32991728 0.48559052 0.45896691 0.2699107 0.3054552
## anger 0.42228982 0.37216696 0.57968800 0.41231204 0.2362157 0.2758024
## fear 0.35053917 0.29426776 0.36039876 0.48185487 0.3302487 0.4424290
## jealousy 1.00000000 0.71476167 0.32747505 0.33473271 0.3455271 0.3160158
## envy 0.71476167 1.00000000 0.32824960 0.31098912 0.3647476 0.3002625
## disgust 0.32747505 0.32824960 1.00000000 0.34191745 0.1886266 0.1987625
## regret 0.33473271 0.31098912 0.34191745 1.00000000 0.2920329 0.2755626
## feelinggood 0.34552711 0.36474756 0.18862662 0.29203285 1.0000000 0.5459916
## relief 0.31601576 0.30026253 0.19876252 0.27556265 0.5459916 1.0000000
## happiness 0.35720512 0.37446203 0.20181465 0.29596580 0.7082595 0.5411710
## complexity 0.30801522 0.28831737 0.31646127 0.29027936 0.2683273 0.2922038
## relevance 0.22417648 0.21487329 0.05647475 0.14190634 0.3601156 0.4721333
## reliability 0.08217972 0.05296204 0.12563177 0.08687899 0.2268563 0.3461385
## relationships 0.39616765 0.33424620 0.25246611 0.35616363 0.2648325 0.3809756
## conflict 0.41732281 0.36112638 0.38628778 0.38301081 0.2563548 0.3045518
## politeness 0.24808658 0.27077784 0.25169215 0.16467008 0.2425720 0.2069083
## notlying 0.33757715 0.31719399 0.28918992 0.34350424 0.2511141 0.2714981
## unsureresp. 0.32185566 0.26922128 0.33453575 0.46456162 0.2666833 0.3092804
## exclusion 0.47001346 0.42006585 0.28669799 0.28585183 0.3479339 0.3772505
## self.percept. 0.37602323 0.34888293 0.28268786 0.28948849 0.3549692 0.4033383
## actiongeneral 0.13711425 0.11375210 0.14307681 0.17427256 0.2736766 0.4096112
## actionnow 0.12935706 0.12011133 0.08626281 0.10647008 0.3063014 0.4110130
## disadvantage 0.42889770 0.38777027 0.26315040 0.36487624 0.3830086 0.4318001
## goalinterfer 0.35846601 0.30877004 0.19524515 0.32160408 0.3642758 0.4562755
## socialobligat 0.11800262 0.11107794 0.24863277 0.16331559 0.1825518 0.2902696
## legalobligat 0.17294352 0.19555192 0.24618512 0.17267126 0.2626834 0.3588674
## surprise 0.29933265 0.30256003 0.25590217 0.26431529 0.4064420 0.2900834
## fairness 0.28005887 0.30858127 0.29494503 0.26464802 0.2552872 0.2799923
## happiness complexity relevance reliability relationships
## expectation 0.2397001 0.2775573 0.25646595 0.18545272 0.3238377
## valence 0.3033323 0.3595164 0.23709839 0.18230139 0.3874311
## arousal 0.3567941 0.3589919 0.25099367 0.20460203 0.3990074
## sadness 0.2897181 0.3708931 0.21882054 0.18111443 0.3952525
## anger 0.2648376 0.3563017 0.17889833 0.17930141 0.3798968
## fear 0.3256109 0.3933162 0.34849877 0.25011472 0.4695966
## jealousy 0.3572051 0.3080152 0.22417648 0.08217972 0.3961677
## envy 0.3744620 0.2883174 0.21487329 0.05296204 0.3342462
## disgust 0.2018147 0.3164613 0.05647475 0.12563177 0.2524661
## regret 0.2959658 0.2902794 0.14190634 0.08687899 0.3561636
## feelinggood 0.7082595 0.2683273 0.36011565 0.22685631 0.2648325
## relief 0.5411710 0.2922038 0.47213332 0.34613846 0.3809756
## happiness 1.0000000 0.2599320 0.34209626 0.24211442 0.3159455
## complexity 0.2599320 1.0000000 0.20005454 0.20389206 0.2999190
## relevance 0.3420963 0.2000545 1.00000000 0.43531037 0.3601105
## reliability 0.2421144 0.2038921 0.43531037 1.00000000 0.2041885
## relationships 0.3159455 0.2999190 0.36011050 0.20418854 1.0000000
## conflict 0.2705714 0.3077800 0.20896677 0.17675834 0.5803009
## politeness 0.2419248 0.3022168 0.10203976 0.13591964 0.3249712
## notlying 0.2567506 0.2770559 0.16899400 0.21300530 0.3975133
## unsureresp. 0.2644877 0.3895889 0.23878891 0.18490224 0.4392956
## exclusion 0.3655887 0.3570022 0.23932733 0.16718768 0.4864089
## self.percept. 0.3524825 0.3434231 0.41481285 0.24540865 0.4459063
## actiongeneral 0.2876833 0.2333411 0.50714043 0.44145075 0.3066937
## actionnow 0.2849052 0.2085702 0.59403411 0.45670151 0.2526948
## disadvantage 0.3794917 0.3508088 0.37106239 0.23948023 0.4725844
## goalinterfer 0.3553300 0.3109882 0.47663120 0.26923115 0.4766255
## socialobligat 0.1938424 0.2701528 0.30086101 0.31891987 0.3194480
## legalobligat 0.2550225 0.3034115 0.29282362 0.31477110 0.3507959
## surprise 0.3677269 0.2871699 0.13605243 0.10865594 0.2074117
## fairness 0.2722792 0.3219180 0.21664997 0.24010093 0.3386755
## conflict politeness notlying unsureresp. exclusion
## expectation 0.3057135 0.1143631 0.2037159 0.4284112 0.2237651
## valence 0.3896549 0.1807060 0.3077725 0.5009429 0.3107659
## arousal 0.3566116 0.2370163 0.2709800 0.4699303 0.3245321
## sadness 0.3804781 0.2207393 0.2821882 0.4915051 0.3181984
## anger 0.4176043 0.2247762 0.2834920 0.4171232 0.3315133
## fear 0.4136184 0.1663450 0.3135511 0.5186386 0.3677919
## jealousy 0.4173228 0.2480866 0.3375772 0.3218557 0.4700135
## envy 0.3611264 0.2707778 0.3171940 0.2692213 0.4200658
## disgust 0.3862878 0.2516922 0.2891899 0.3345358 0.2866980
## regret 0.3830108 0.1646701 0.3435042 0.4645616 0.2858518
## feelinggood 0.2563548 0.2425720 0.2511141 0.2666833 0.3479339
## relief 0.3045518 0.2069083 0.2714981 0.3092804 0.3772505
## happiness 0.2705714 0.2419248 0.2567506 0.2644877 0.3655887
## complexity 0.3077800 0.3022168 0.2770559 0.3895889 0.3570022
## relevance 0.2089668 0.1020398 0.1689940 0.2387889 0.2393273
## reliability 0.1767583 0.1359196 0.2130053 0.1849022 0.1671877
## relationships 0.5803009 0.3249712 0.3975133 0.4392956 0.4864089
## conflict 1.0000000 0.3556785 0.4480701 0.4349366 0.4695326
## politeness 0.3556785 1.0000000 0.3290549 0.2784077 0.3722767
## notlying 0.4480701 0.3290549 1.0000000 0.3417610 0.4273780
## unsureresp. 0.4349366 0.2784077 0.3417610 1.0000000 0.3415824
## exclusion 0.4695326 0.3722767 0.4273780 0.3415824 1.0000000
## self.percept. 0.3293225 0.2605216 0.2922205 0.3973499 0.3968105
## actiongeneral 0.2190078 0.1703420 0.2371201 0.3153525 0.2234973
## actionnow 0.1735797 0.1022710 0.1870817 0.1906053 0.2145626
## disadvantage 0.4124109 0.2501756 0.3968018 0.4062718 0.4670470
## goalinterfer 0.3391966 0.1923098 0.2987200 0.3561290 0.3957628
## socialobligat 0.3092120 0.3009263 0.3032635 0.3273723 0.2937801
## legalobligat 0.3585770 0.2998407 0.4204491 0.2984373 0.3573164
## surprise 0.2052270 0.2816259 0.2375723 0.2328059 0.3116909
## fairness 0.3874847 0.3818929 0.5090895 0.3320857 0.3910960
## self.percept. actiongeneral actionnow disadvantage goalinterfer
## expectation 0.3494316 0.2783822 0.23068144 0.3483148 0.3181815
## valence 0.4407834 0.2648632 0.18519085 0.4164990 0.3870351
## arousal 0.4291907 0.2850313 0.21638285 0.3594263 0.3287547
## sadness 0.4290053 0.2665760 0.17460612 0.3543468 0.3658174
## anger 0.4007583 0.2288970 0.16177600 0.3638942 0.3141622
## fear 0.4295134 0.3605213 0.28880311 0.4692187 0.4715779
## jealousy 0.3760232 0.1371143 0.12935706 0.4288977 0.3584660
## envy 0.3488829 0.1137521 0.12011133 0.3877703 0.3087700
## disgust 0.2826879 0.1430768 0.08626281 0.2631504 0.1952452
## regret 0.2894885 0.1742726 0.10647008 0.3648762 0.3216041
## feelinggood 0.3549692 0.2736766 0.30630140 0.3830086 0.3642758
## relief 0.4033383 0.4096112 0.41101301 0.4318001 0.4562755
## happiness 0.3524825 0.2876833 0.28490519 0.3794917 0.3553300
## complexity 0.3434231 0.2333411 0.20857025 0.3508088 0.3109882
## relevance 0.4148129 0.5071404 0.59403411 0.3710624 0.4766312
## reliability 0.2454086 0.4414508 0.45670151 0.2394802 0.2692312
## relationships 0.4459063 0.3066937 0.25269479 0.4725844 0.4766255
## conflict 0.3293225 0.2190078 0.17357968 0.4124109 0.3391966
## politeness 0.2605216 0.1703420 0.10227103 0.2501756 0.1923098
## notlying 0.2922205 0.2371201 0.18708169 0.3968018 0.2987200
## unsureresp. 0.3973499 0.3153525 0.19060530 0.4062718 0.3561290
## exclusion 0.3968105 0.2234973 0.21456259 0.4670470 0.3957628
## self.percept. 1.0000000 0.3619289 0.34263443 0.4317848 0.4841495
## actiongeneral 0.3619289 1.0000000 0.55488727 0.3352735 0.3989646
## actionnow 0.3426344 0.5548873 1.00000000 0.3076829 0.3953785
## disadvantage 0.4317848 0.3352735 0.30768291 1.0000000 0.5611209
## goalinterfer 0.4841495 0.3989646 0.39537848 0.5611209 1.0000000
## socialobligat 0.3775174 0.4764008 0.36613091 0.2876636 0.2825382
## legalobligat 0.2973847 0.4103124 0.33265008 0.3846550 0.3264586
## surprise 0.2458130 0.1367014 0.14035499 0.3050319 0.2314414
## fairness 0.3207610 0.2717477 0.23703921 0.3842195 0.2856209
## socialobligat legalobligat surprise fairness
## expectation 0.2642331 0.2114515 0.2024977 0.1707168
## valence 0.2765725 0.2467779 0.2226430 0.2487619
## arousal 0.3250004 0.2701334 0.3404616 0.2622549
## sadness 0.2965516 0.2388927 0.2424084 0.2522314
## anger 0.2943342 0.2741916 0.2122025 0.2632953
## fear 0.3209490 0.3217720 0.2379468 0.2557432
## jealousy 0.1180026 0.1729435 0.2993326 0.2800589
## envy 0.1110779 0.1955519 0.3025600 0.3085813
## disgust 0.2486328 0.2461851 0.2559022 0.2949450
## regret 0.1633156 0.1726713 0.2643153 0.2646480
## feelinggood 0.1825518 0.2626834 0.4064420 0.2552872
## relief 0.2902696 0.3588674 0.2900834 0.2799923
## happiness 0.1938424 0.2550225 0.3677269 0.2722792
## complexity 0.2701528 0.3034115 0.2871699 0.3219180
## relevance 0.3008610 0.2928236 0.1360524 0.2166500
## reliability 0.3189199 0.3147711 0.1086559 0.2401009
## relationships 0.3194480 0.3507959 0.2074117 0.3386755
## conflict 0.3092120 0.3585770 0.2052270 0.3874847
## politeness 0.3009263 0.2998407 0.2816259 0.3818929
## notlying 0.3032635 0.4204491 0.2375723 0.5090895
## unsureresp. 0.3273723 0.2984373 0.2328059 0.3320857
## exclusion 0.2937801 0.3573164 0.3116909 0.3910960
## self.percept. 0.3775174 0.2973847 0.2458130 0.3207610
## actiongeneral 0.4764008 0.4103124 0.1367014 0.2717477
## actionnow 0.3661309 0.3326501 0.1403550 0.2370392
## disadvantage 0.2876636 0.3846550 0.3050319 0.3842195
## goalinterfer 0.2825382 0.3264586 0.2314414 0.2856209
## socialobligat 1.0000000 0.5647636 0.1182733 0.3727384
## legalobligat 0.5647636 1.0000000 0.2179846 0.4211929
## surprise 0.1182733 0.2179846 1.0000000 0.2415213
## fairness 0.3727384 0.4211929 0.2415213 1.0000000
#correlation matrix for category
mat_corr <- corr_overall$cor_matrix[[1]]
#long format
mat_long_corr <- melt(mat_corr, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_corr, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: Overall")
strong_corr <- mat_long_corr %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
Plot zeigt positive Korrelationen über .5, da es nur positive Korr über .5 gibt, wird hier nicht der Betrag von r dargestellt
ggplot(strong_corr, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") + # Felder mit Rahmen
scale_fill_gradient2(
mid = "white", # 0
high = "red", # positive Korrelationen
midpoint = 0,
limits = c(0.5, 1)
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_blank(),
panel.grid = element_blank()
) +
ggtitle("Strong correlations (r > 0.5)")
## 10.3. Correlation Matrix by Scenario and DI Response
corr_overall_DI <- dat_short %>%
filter(want_to_know_bin == 1) %>%
summarise(
cor_matrix = list(
cor(
mutate(across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
mat_corr_DI <- corr_overall_DI$cor_matrix[[1]]
mat_long_corr_DI <- melt(mat_corr_DI, varnames = c("Var1", "Var2"), value.name = "r")
strong_corr_DI <- mat_long_corr_DI %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
corr_overall_noDI <- dat_short %>%
filter(want_to_know_bin == 0) %>%
summarise(
cor_matrix = list(
cor(
mutate(across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
mat_corr_noDI <- corr_overall_noDI$cor_matrix[[1]]
mat_long_corr_noDI <- melt(mat_corr_noDI, varnames = c("Var1", "Var2"), value.name = "r")
strong_corr_noDI <- mat_long_corr_noDI %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
corr_scenario_DI <- dat_short %>%
filter(want_to_know_bin == 1) %>%
group_by(Scenario_Content) %>%
summarise(
cor_matrix = list(
cor(
as.data.frame(across(all_of(motive_vars)) %>%
mutate(across(everything(), ~ ifelse(.x == -99, NA, .x)))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `cor_matrix = list(...)`.
## ℹ In group 22: `Scenario_Content = "nutritional_deficiencies"`.
## Caused by warning in `cor()`:
## ! the standard deviation is zero
corr_scenario_DI$cor_matrix[[which(corr_scenario$Scenario_Content == "nutritional_deficiencies")]]
## expectation valence arousal sadness anger
## expectation 1.0000000 0.90224364 0.5254237 0.90224364 0.74581524
## valence 0.9022436 1.00000000 0.8142199 1.00000000 0.86640023
## arousal 0.5254237 0.81421987 1.0000000 0.81421987 0.90282897
## sadness 0.9022436 1.00000000 0.8142199 1.00000000 0.86640023
## anger 0.7458152 0.86640023 0.9028290 0.86640023 1.00000000
## fear 0.5367826 0.77892406 0.9788389 0.77892406 0.95065415
## jealousy -0.1177603 -0.05096472 0.3532809 -0.05096472 0.45454545
## envy 0.9449112 0.86602540 0.5000000 0.86602540 0.86602540
## disgust 0.6381723 0.88571429 0.8142199 0.88571429 0.66254135
## regret 0.9057257 0.96214047 0.8233870 0.96214047 0.95346259
## feelinggood 0.5240003 0.83152184 0.8733338 0.83152184 0.67419986
## relief 0.8268106 0.87831007 0.5261522 0.87831007 0.52223297
## happiness 0.6272151 0.89190174 0.9856237 0.89190174 0.89922880
## complexity 0.8660254 0.99587059 0.8660254 0.99587059 0.98198051
## relevance 0.0000000 0.00000000 -0.3188964 0.00000000 -0.49236596
## reliability -0.2747740 0.15289416 0.5102946 0.15289416 0.09090909
## relationships -0.9897783 -0.83152184 -0.4075558 -0.83152184 -0.67419986
## conflict -0.8660254 -0.57655666 0.0000000 -0.57655666 -0.32732684
## politeness 0.8260332 0.98624138 0.9011271 0.98624138 0.99339927
## notlying 0.3273268 0.69337525 0.9819805 0.69337525 0.86602540
## unsureresp. 0.5254237 0.81421987 1.0000000 0.81421987 0.90282897
## exclusion -0.9449112 -0.72057669 -0.1889822 -0.72057669 -0.50000000
## self.percept. 0.5641519 0.84515425 0.9981150 0.84515425 0.90453403
## actiongeneral 0.5641519 0.84515425 0.9981150 0.84515425 0.90453403
## actionnow 0.7559289 0.96076892 0.9449112 0.96076892 1.00000000
## disadvantage 0.6745135 0.92309308 0.9661950 0.92309308 0.88662069
## goalinterfer 0.3682298 0.47809144 0.7364597 0.47809144 0.85280287
## socialobligat 0.1540416 0.37142857 0.7702080 0.37142857 0.76447079
## legalobligat NA NA NA NA NA
## surprise -0.3905667 -0.16903085 0.3905667 -0.16903085 0.30151134
## fairness -0.6546537 -0.27735010 0.3273268 -0.27735010 0.00000000
## fear jealousy envy disgust regret
## expectation 0.5367826 -0.11776030 0.9449112 0.6381723 0.9057257
## valence 0.7789241 -0.05096472 0.8660254 0.8857143 0.9621405
## arousal 0.9788389 0.35328090 0.5000000 0.8142199 0.8233870
## sadness 0.7789241 -0.05096472 0.8660254 0.8857143 0.9621405
## anger 0.9506542 0.45454545 0.8660254 0.6625413 0.9534626
## fear 1.0000000 0.51189070 0.6546537 0.6969321 0.8436615
## jealousy 0.5118907 1.00000000 0.8660254 -0.2548236 0.1906925
## envy 0.6546537 0.86602540 1.0000000 0.0000000 0.9449112
## disgust 0.6969321 -0.25482360 0.0000000 1.0000000 0.7483315
## regret 0.8436615 0.19069252 0.9449112 0.7483315 1.0000000
## feelinggood 0.7592566 -0.13483997 0.0000000 0.9827076 0.7071068
## relief 0.4200840 -0.52223297 NA 0.8783101 0.7302967
## happiness 0.9459053 0.20751434 0.5000000 0.8919017 0.8705715
## complexity 0.8660254 -0.18898224 NA 0.9819805 0.9958706
## relevance -0.4950738 -0.98473193 -0.9449112 0.2760262 -0.2581989
## reliability 0.3656362 -0.09090909 -0.5000000 0.5606119 0.0000000
## relationships -0.4338609 0.13483997 -0.8660254 -0.5291503 -0.8485281
## conflict 0.0000000 0.94491118 NA -0.3273268 -0.5765567
## politeness 0.9011271 -0.11470787 NA 0.9933993 0.9862414
## notlying 0.9819805 0.50000000 NA 0.8660254 0.6933752
## unsureresp. 0.9788389 0.35328090 0.5000000 0.8142199 0.8233870
## exclusion -0.1889822 0.86602540 NA -0.5000000 -0.7205767
## self.percept. 0.9701425 0.30151134 0.5000000 0.8451543 0.8432740
## actiongeneral 0.9701425 0.30151134 0.5000000 0.8451543 0.8432740
## actionnow 0.9449112 0.00000000 NA 1.0000000 0.9607689
## disadvantage 0.9169681 0.12666010 0.5000000 0.9230931 0.8856149
## goalinterfer 0.8574929 0.85280287 0.8660254 0.2390457 0.6708204
## socialobligat 0.8609161 0.86640023 0.6933752 0.2571429 0.5345225
## legalobligat NA NA NA NA NA
## surprise 0.4850713 0.90453403 0.5000000 -0.1690309 0.0000000
## fairness 0.3273268 1.00000000 NA 0.0000000 -0.2773501
## feelinggood relief happiness complexity relevance
## expectation 0.5240003 0.82681063 0.6272151 0.8660254 0.0000000
## valence 0.8315218 0.87831007 0.8919017 0.9958706 0.0000000
## arousal 0.8733338 0.52615222 0.9856237 0.8660254 -0.3188964
## sadness 0.8315218 0.87831007 0.8919017 0.9958706 0.0000000
## anger 0.6741999 0.52223297 0.8992288 0.9819805 -0.4923660
## fear 0.7592566 0.42008403 0.9459053 0.8660254 -0.4950738
## jealousy -0.1348400 -0.52223297 0.2075143 -0.1889822 -0.9847319
## envy 0.0000000 NA 0.5000000 NA -0.9449112
## disgust 0.9827076 0.87831007 0.8919017 0.9819805 0.2760262
## regret 0.7071068 0.73029674 0.8705715 0.9958706 -0.2581989
## feelinggood 1.0000000 0.77459667 0.9233805 0.9285714 0.1825742
## relief 0.7745967 1.00000000 0.6622662 0.9449112 0.4714045
## happiness 0.9233805 0.66226618 1.0000000 0.9285714 -0.1873172
## complexity 0.9285714 0.94491118 0.9285714 1.0000000 0.1889822
## relevance 0.1825742 0.47140452 -0.1873172 0.1889822 1.0000000
## reliability 0.6741999 0.17407766 0.4842001 0.3273268 0.2461830
## relationships -0.4000000 -0.77459667 -0.5129892 -0.7857143 0.0000000
## conflict -0.1428571 -0.75592895 -0.1428571 -0.5000000 -0.9449112
## politeness 0.9538210 0.91766294 0.9538210 0.9971765 0.1147079
## notlying 0.9449112 0.50000000 0.9449112 0.7559289 -0.5000000
## unsureresp. 0.8733338 0.52615222 0.9856237 0.8660254 -0.3188964
## exclusion -0.3273268 -0.86602540 -0.3273268 -0.6546537 -0.8660254
## self.percept. 0.8944272 0.57735027 0.9941348 0.8910421 -0.2721655
## actiongeneral 0.8944272 0.57735027 0.9941348 0.8910421 -0.2721655
## actionnow 0.9819805 0.86602540 0.9819805 0.9819805 0.0000000
## disadvantage 0.9393364 0.72760688 0.9958635 0.9538210 -0.1143324
## goalinterfer 0.3162278 0.00000000 0.6488857 0.7559289 -0.8660254
## socialobligat 0.3779645 -0.09759001 0.6592317 0.5000000 -0.8280787
## legalobligat NA NA NA NA NA
## surprise 0.0000000 -0.57735027 0.2294157 -0.1889822 -0.8164966
## fairness 0.1889822 -0.50000000 0.1889822 -0.1889822 -1.0000000
## reliability relationships conflict politeness notlying
## expectation -0.27477404 -0.98977827 -0.86602540 0.8260332 0.3273268
## valence 0.15289416 -0.83152184 -0.57655666 0.9862414 0.6933752
## arousal 0.51029464 -0.40755576 0.00000000 0.9011271 0.9819805
## sadness 0.15289416 -0.83152184 -0.57655666 0.9862414 0.6933752
## anger 0.09090909 -0.67419986 -0.32732684 0.9933993 0.8660254
## fear 0.36563621 -0.43386092 0.00000000 0.9011271 0.9819805
## jealousy -0.09090909 0.13483997 0.94491118 -0.1147079 0.5000000
## envy -0.50000000 -0.86602540 NA NA NA
## disgust 0.56061191 -0.52915026 -0.32732684 0.9933993 0.8660254
## regret 0.00000000 -0.84852814 -0.57655666 0.9862414 0.6933752
## feelinggood 0.67419986 -0.40000000 -0.14285714 0.9538210 0.9449112
## relief 0.17407766 -0.77459667 -0.75592895 0.9176629 0.5000000
## happiness 0.48420012 -0.51298918 -0.14285714 0.9538210 0.9449112
## complexity 0.32732684 -0.78571429 -0.50000000 0.9971765 0.7559289
## relevance 0.24618298 0.00000000 -0.94491118 0.1147079 -0.5000000
## reliability 1.00000000 0.40451992 0.65465367 0.3973597 0.8660254
## relationships 0.40451992 1.00000000 0.92857143 -0.7370435 -0.1889822
## conflict 0.65465367 0.92857143 1.00000000 -0.4335550 0.1889822
## politeness 0.39735971 -0.73704347 -0.43355498 1.0000000 0.8029551
## notlying 0.86602540 -0.18898224 0.18898224 0.8029551 1.0000000
## unsureresp. 0.51029464 -0.40755576 0.00000000 0.9011271 0.9819805
## exclusion 0.50000000 0.98198051 0.98198051 -0.5960396 0.0000000
## self.percept. 0.50251891 -0.44721360 -0.05241424 0.9226129 0.9707253
## actiongeneral 0.50251891 -0.44721360 -0.05241424 0.9226129 0.9707253
## actionnow 0.50000000 -0.65465367 -0.32732684 0.9933993 0.8660254
## disadvantage 0.46442036 -0.56360186 -0.21677749 0.9736842 0.9176629
## goalinterfer 0.00000000 -0.31622777 0.18898224 0.8029551 1.0000000
## socialobligat 0.25482360 -0.07559289 0.50000000 0.5636215 0.9449112
## legalobligat NA NA NA NA NA
## surprise 0.30151134 0.44721360 0.94491118 -0.1147079 0.5000000
## fairness 0.86602540 0.75592895 0.94491118 -0.1147079 0.5000000
## unsureresp. exclusion self.percept. actiongeneral actionnow
## expectation 0.5254237 -0.9449112 0.56415195 0.56415195 0.7559289
## valence 0.8142199 -0.7205767 0.84515425 0.84515425 0.9607689
## arousal 1.0000000 -0.1889822 0.99811498 0.99811498 0.9449112
## sadness 0.8142199 -0.7205767 0.84515425 0.84515425 0.9607689
## anger 0.9028290 -0.5000000 0.90453403 0.90453403 1.0000000
## fear 0.9788389 -0.1889822 0.97014250 0.97014250 0.9449112
## jealousy 0.3532809 0.8660254 0.30151134 0.30151134 0.0000000
## envy 0.5000000 NA 0.50000000 0.50000000 NA
## disgust 0.8142199 -0.5000000 0.84515425 0.84515425 1.0000000
## regret 0.8233870 -0.7205767 0.84327404 0.84327404 0.9607689
## feelinggood 0.8733338 -0.3273268 0.89442719 0.89442719 0.9819805
## relief 0.5261522 -0.8660254 0.57735027 0.57735027 0.8660254
## happiness 0.9856237 -0.3273268 0.99413485 0.99413485 0.9819805
## complexity 0.8660254 -0.6546537 0.89104211 0.89104211 0.9819805
## relevance -0.3188964 -0.8660254 -0.27216553 -0.27216553 0.0000000
## reliability 0.5102946 0.5000000 0.50251891 0.50251891 0.5000000
## relationships -0.4075558 0.9819805 -0.44721360 -0.44721360 -0.6546537
## conflict 0.0000000 0.9819805 -0.05241424 -0.05241424 -0.3273268
## politeness 0.9011271 -0.5960396 0.92261291 0.92261291 0.9933993
## notlying 0.9819805 0.0000000 0.97072534 0.97072534 0.8660254
## unsureresp. 1.0000000 -0.1889822 0.99811498 0.99811498 0.9449112
## exclusion -0.1889822 1.0000000 -0.24019223 -0.24019223 -0.5000000
## self.percept. 0.9981150 -0.2401922 1.00000000 1.00000000 0.9607689
## actiongeneral 0.9981150 -0.2401922 1.00000000 1.00000000 0.9607689
## actionnow 0.9449112 -0.5000000 0.96076892 0.96076892 1.0000000
## disadvantage 0.9661950 -0.3973597 0.98019606 0.98019606 0.9933993
## goalinterfer 0.7364597 0.0000000 0.70710678 0.70710678 0.8660254
## socialobligat 0.7702080 0.3273268 0.73246702 0.73246702 0.6546537
## legalobligat NA NA NA NA NA
## surprise 0.3905667 0.8660254 0.33333333 0.33333333 0.0000000
## fairness 0.3273268 0.8660254 0.27735010 0.27735010 0.0000000
## disadvantage goalinterfer socialobligat legalobligat surprise
## expectation 0.6745135 0.3682298 0.15404160 NA -0.3905667
## valence 0.9230931 0.4780914 0.37142857 NA -0.1690309
## arousal 0.9661950 0.7364597 0.77020798 NA 0.3905667
## sadness 0.9230931 0.4780914 0.37142857 NA -0.1690309
## anger 0.8866207 0.8528029 0.76447079 NA 0.3015113
## fear 0.9169681 0.8574929 0.86091606 NA 0.4850713
## jealousy 0.1266601 0.8528029 0.86640023 NA 0.9045340
## envy 0.5000000 0.8660254 0.69337525 NA 0.5000000
## disgust 0.9230931 0.2390457 0.25714286 NA -0.1690309
## regret 0.8856149 0.6708204 0.53452248 NA 0.0000000
## feelinggood 0.9393364 0.3162278 0.37796447 NA 0.0000000
## relief 0.7276069 0.0000000 -0.09759001 NA -0.5773503
## happiness 0.9958635 0.6488857 0.65923172 NA 0.2294157
## complexity 0.9538210 0.7559289 0.50000000 NA -0.1889822
## relevance -0.1143324 -0.8660254 -0.82807867 NA -0.8164966
## reliability 0.4644204 0.0000000 0.25482360 NA 0.3015113
## relationships -0.5636019 -0.3162278 -0.07559289 NA 0.4472136
## conflict -0.2167775 0.1889822 0.50000000 NA 0.9449112
## politeness 0.9736842 0.8029551 0.56362148 NA -0.1147079
## notlying 0.9176629 1.0000000 0.94491118 NA 0.5000000
## unsureresp. 0.9661950 0.7364597 0.77020798 NA 0.3905667
## exclusion -0.3973597 0.0000000 0.32732684 NA 0.8660254
## self.percept. 0.9801961 0.7071068 0.73246702 NA 0.3333333
## actiongeneral 0.9801961 0.7071068 0.73246702 NA 0.3333333
## actionnow 0.9933993 0.8660254 0.65465367 NA 0.0000000
## disadvantage 1.0000000 0.5940885 0.59172634 NA 0.1400280
## goalinterfer 0.5940885 1.0000000 0.95618289 NA 0.7071068
## socialobligat 0.5917263 0.9561829 1.00000000 NA 0.8451543
## legalobligat NA NA NA NA NA
## surprise 0.1400280 0.7071068 0.84515425 NA 1.0000000
## fairness 0.1147079 0.5000000 0.75592895 NA 1.0000000
## fairness
## expectation -0.6546537
## valence -0.2773501
## arousal 0.3273268
## sadness -0.2773501
## anger 0.0000000
## fear 0.3273268
## jealousy 1.0000000
## envy NA
## disgust 0.0000000
## regret -0.2773501
## feelinggood 0.1889822
## relief -0.5000000
## happiness 0.1889822
## complexity -0.1889822
## relevance -1.0000000
## reliability 0.8660254
## relationships 0.7559289
## conflict 0.9449112
## politeness -0.1147079
## notlying 0.5000000
## unsureresp. 0.3273268
## exclusion 0.8660254
## self.percept. 0.2773501
## actiongeneral 0.2773501
## actionnow 0.0000000
## disadvantage 0.1147079
## goalinterfer 0.5000000
## socialobligat 0.7559289
## legalobligat NA
## surprise 1.0000000
## fairness 1.0000000
mat_got_DI <- corr_scenario_DI$cor_matrix[[which(corr_scenario$Scenario_Content == "ending_GoT")]]
mat_long_got_DI <- melt(mat_got_DI, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_got_DI, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: ending GoT DI")
strong_got_DI <- mat_long_got_DI %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
corr_scenario_noDI <- dat_short %>%
filter(want_to_know_bin == 0) %>%
group_by(Scenario_Content) %>%
summarise(
cor_matrix = list(
cor(
as.data.frame(across(all_of(motive_vars)) %>%
mutate(across(everything(), ~ ifelse(.x == -99, NA, .x)))),
use = "pairwise.complete.obs"
)
),
.groups = "drop"
)
corr_scenario_noDI$cor_matrix[[which(corr_scenario$Scenario_Content == "nutritional_deficiencies")]]
## expectation valence arousal sadness anger
## expectation 1.00000000 0.50756887 0.40747208 0.35536810 0.40269175
## valence 0.50756887 1.00000000 0.70882879 0.69338302 0.64157032
## arousal 0.40747208 0.70882879 1.00000000 0.59863630 0.50791663
## sadness 0.35536810 0.69338302 0.59863630 1.00000000 0.69933363
## anger 0.40269175 0.64157032 0.50791663 0.69933363 1.00000000
## fear 0.48521716 0.65263250 0.65636846 0.60716959 0.47291711
## jealousy 0.50445861 0.48997421 0.46408811 0.52742876 0.64275615
## envy 0.43723557 0.56858340 0.43267832 0.68078565 0.68791976
## disgust 0.42040720 0.52321076 0.52458437 0.59233409 0.56771037
## regret 0.43263790 0.55428568 0.55909763 0.63393937 0.44906503
## feelinggood 0.31550658 0.47231291 0.47304654 0.42878757 0.30601004
## relief 0.24034165 0.36541895 0.45949891 0.32376513 0.20109279
## happiness 0.39341813 0.54281251 0.52995976 0.42178205 0.49782742
## complexity 0.37817534 0.46217687 0.41449852 0.35557337 0.38475028
## relevance 0.02172106 0.02369033 -0.02880198 0.00507797 -0.04667937
## reliability -0.07761275 -0.03291809 0.03541059 -0.12794684 -0.01254650
## relationships 0.23339996 0.42968392 0.43929761 0.44737299 0.36637434
## conflict 0.23452287 0.41013911 0.59265042 0.45773175 0.32881421
## politeness 0.21153924 0.44681315 0.55670416 0.29652955 0.38563190
## notlying 0.24547515 0.39174075 0.34925260 0.27857439 0.45430505
## unsureresp. 0.39227330 0.33314554 0.31471606 0.44164885 0.41896091
## exclusion 0.37488488 0.60086298 0.62133673 0.64263042 0.61581286
## self.percept. 0.21663601 0.29470611 0.17691634 0.30344222 0.29456503
## actiongeneral 0.08378885 0.18591326 0.15982691 0.06689318 0.01016876
## actionnow 0.15428808 0.07310046 0.11706301 0.08386663 0.04014657
## disadvantage 0.47136725 0.44383993 0.50364130 0.42605258 0.51994169
## goalinterfer 0.22447773 0.38053997 0.41384353 0.49832350 0.44654456
## socialobligat 0.24102440 0.27068457 0.43721261 0.22187142 0.19203064
## legalobligat 0.17808274 0.30639221 0.35089125 0.27876169 0.35044816
## surprise 0.29827511 0.37030262 0.50720410 0.51896736 0.40035685
## fairness 0.14161404 0.34615068 0.28078650 0.34104939 0.42679437
## fear jealousy envy disgust regret
## expectation 0.485217162 0.504458607 0.43723557 0.420407201 0.43263790
## valence 0.652632504 0.489974208 0.56858340 0.523210758 0.55428568
## arousal 0.656368456 0.464088110 0.43267832 0.524584372 0.55909763
## sadness 0.607169591 0.527428755 0.68078565 0.592334092 0.63393937
## anger 0.472917108 0.642756151 0.68791976 0.567710370 0.44906503
## fear 1.000000000 0.501233759 0.42192079 0.455312898 0.56520309
## jealousy 0.501233759 1.000000000 0.63511260 0.573027355 0.49966714
## envy 0.421920792 0.635112601 1.00000000 0.682250651 0.52265109
## disgust 0.455312898 0.573027355 0.68225065 1.000000000 0.63274007
## regret 0.565203088 0.499667137 0.52265109 0.632740073 1.00000000
## feelinggood 0.468551753 0.204541233 0.35196652 0.357377478 0.37449702
## relief 0.419834749 0.322822968 0.22467737 0.205977270 0.31278750
## happiness 0.425607033 0.325998244 0.39772944 0.323225055 0.32710280
## complexity 0.481688470 0.451457089 0.33729964 0.198600577 0.32322085
## relevance -0.008509743 -0.051233529 0.01181541 -0.300154738 -0.05118471
## reliability -0.102221185 0.097354096 0.07413667 -0.064594150 -0.06077961
## relationships 0.438403681 0.297344573 0.43815642 0.433327589 0.45957973
## conflict 0.466195797 0.242090226 0.41547534 0.553157447 0.58426018
## politeness 0.395339095 0.312508560 0.35835166 0.379606564 0.29282176
## notlying 0.352279374 0.384227996 0.38604178 0.532024073 0.34915639
## unsureresp. 0.384733092 0.439277571 0.46331370 0.291287522 0.34865467
## exclusion 0.513207490 0.527077954 0.55869872 0.718996270 0.59252629
## self.percept. 0.291320971 0.269742231 0.33484367 0.292342160 0.20279698
## actiongeneral 0.169848720 0.009359353 0.03592996 0.004131042 0.15581739
## actionnow 0.002665470 0.063743949 0.11513492 -0.105194636 0.05637086
## disadvantage 0.392048950 0.422911862 0.42114370 0.367533672 0.44717641
## goalinterfer 0.412334921 0.399369296 0.49051589 0.361050417 0.43378830
## socialobligat 0.343366431 0.287976403 0.24552471 0.303460515 0.24805682
## legalobligat 0.254743682 0.286899675 0.35089077 0.413845716 0.29494534
## surprise 0.429005371 0.438502456 0.44116045 0.557364909 0.37955473
## fairness 0.267676791 0.295736432 0.35412367 0.471935093 0.41236412
## feelinggood relief happiness complexity relevance
## expectation 0.31550658 0.24034165 0.3934181 0.3781753 0.021721061
## valence 0.47231291 0.36541895 0.5428125 0.4621769 0.023690327
## arousal 0.47304654 0.45949891 0.5299598 0.4144985 -0.028801979
## sadness 0.42878757 0.32376513 0.4217821 0.3555734 0.005077970
## anger 0.30601004 0.20109279 0.4978274 0.3847503 -0.046679367
## fear 0.46855175 0.41983475 0.4256070 0.4816885 -0.008509743
## jealousy 0.20454123 0.32282297 0.3259982 0.4514571 -0.051233529
## envy 0.35196652 0.22467737 0.3977294 0.3372996 0.011815412
## disgust 0.35737748 0.20597727 0.3232251 0.1986006 -0.300154738
## regret 0.37449702 0.31278750 0.3271028 0.3232208 -0.051184711
## feelinggood 1.00000000 0.61266761 0.6377236 0.3656149 0.076440062
## relief 0.61266761 1.00000000 0.5704244 0.4275381 0.222274937
## happiness 0.63772357 0.57042442 1.0000000 0.3190419 0.151430147
## complexity 0.36561488 0.42753812 0.3190419 1.0000000 0.131079293
## relevance 0.07644006 0.22227494 0.1514301 0.1310793 1.000000000
## reliability 0.08133406 0.21677449 0.1163650 0.1055344 0.360814250
## relationships 0.43775351 0.29198049 0.4751129 0.4460179 0.050347757
## conflict 0.47874998 0.28633764 0.4439669 0.2708745 -0.020974319
## politeness 0.35279107 0.18764645 0.4160233 0.3110270 -0.062919373
## notlying 0.25526845 0.06780258 0.3200424 0.2946953 -0.237902380
## unsureresp. 0.27857714 0.18438889 0.1805470 0.5331399 0.090415845
## exclusion 0.45662986 0.31156652 0.4687154 0.4133360 -0.150824361
## self.percept. 0.30477657 0.18113550 0.2476417 0.3386084 0.172085003
## actiongeneral 0.37732729 0.52153934 0.3552128 0.2157974 0.465664726
## actionnow 0.16111200 0.21087122 0.2907517 -0.0390589 0.609447145
## disadvantage 0.37547806 0.23466969 0.3815097 0.4130174 0.090876686
## goalinterfer 0.42124449 0.32807969 0.4909687 0.2118347 0.058869782
## socialobligat 0.32461607 0.27698688 0.4535561 0.3793059 0.160920909
## legalobligat 0.30662275 0.20575839 0.2824792 0.4547579 0.012120353
## surprise 0.32350914 0.27083988 0.3011490 0.3123142 -0.128450120
## fairness 0.16211679 0.04883930 0.3327058 0.3225669 -0.118586998
## reliability relationships conflict politeness notlying
## expectation -0.077612745 0.23339996 0.23452287 0.21153924 0.24547515
## valence -0.032918090 0.42968392 0.41013911 0.44681315 0.39174075
## arousal 0.035410587 0.43929761 0.59265042 0.55670416 0.34925260
## sadness -0.127946839 0.44737299 0.45773175 0.29652955 0.27857439
## anger -0.012546498 0.36637434 0.32881421 0.38563190 0.45430505
## fear -0.102221185 0.43840368 0.46619580 0.39533910 0.35227937
## jealousy 0.097354096 0.29734457 0.24209023 0.31250856 0.38422800
## envy 0.074136668 0.43815642 0.41547534 0.35835166 0.38604178
## disgust -0.064594150 0.43332759 0.55315745 0.37960656 0.53202407
## regret -0.060779614 0.45957973 0.58426018 0.29282176 0.34915639
## feelinggood 0.081334063 0.43775351 0.47874998 0.35279107 0.25526845
## relief 0.216774494 0.29198049 0.28633764 0.18764645 0.06780258
## happiness 0.116365025 0.47511291 0.44396694 0.41602331 0.32004235
## complexity 0.105534373 0.44601790 0.27087445 0.31102700 0.29469532
## relevance 0.360814250 0.05034776 -0.02097432 -0.06291937 -0.23790238
## reliability 1.000000000 0.02644397 -0.03033917 0.03165250 0.02114553
## relationships 0.026443966 1.00000000 0.63826194 0.43349731 0.60669998
## conflict -0.030339169 0.63826194 1.00000000 0.50201380 0.46261094
## politeness 0.031652502 0.43349731 0.50201380 1.00000000 0.36401348
## notlying 0.021145534 0.60669998 0.46261094 0.36401348 1.00000000
## unsureresp. -0.066927880 0.39576960 0.18216885 0.08098804 0.30820859
## exclusion -0.022841843 0.68197473 0.69676999 0.53474953 0.62809134
## self.percept. -0.002888018 0.40891139 0.15538879 0.36093595 0.18686875
## actiongeneral 0.315875981 0.11014485 0.08533249 0.09560039 -0.15145336
## actionnow 0.330981093 0.08916321 0.02860439 -0.05349988 -0.21645301
## disadvantage 0.049927473 0.52625146 0.43201179 0.35480905 0.41186255
## goalinterfer 0.084244635 0.50270858 0.37848841 0.35159045 0.33104194
## socialobligat 0.066905248 0.34921242 0.26700066 0.42690835 0.20797168
## legalobligat -0.006494485 0.56322939 0.21020338 0.44609376 0.50822012
## surprise -0.079318154 0.31312312 0.44204042 0.48318632 0.32220368
## fairness 0.034561349 0.57633222 0.39248788 0.28299585 0.70116509
## unsureresp. exclusion self.percept. actiongeneral actionnow
## expectation 0.392273300 0.37488488 0.216636014 0.083788845 0.15428808
## valence 0.333145542 0.60086298 0.294706107 0.185913261 0.07310046
## arousal 0.314716060 0.62133673 0.176916337 0.159826911 0.11706301
## sadness 0.441648854 0.64263042 0.303442221 0.066893180 0.08386663
## anger 0.418960911 0.61581286 0.294565035 0.010168764 0.04014657
## fear 0.384733092 0.51320749 0.291320971 0.169848720 0.00266547
## jealousy 0.439277571 0.52707795 0.269742231 0.009359353 0.06374395
## envy 0.463313699 0.55869872 0.334843669 0.035929962 0.11513492
## disgust 0.291287522 0.71899627 0.292342160 0.004131042 -0.10519464
## regret 0.348654674 0.59252629 0.202796976 0.155817393 0.05637086
## feelinggood 0.278577136 0.45662986 0.304776571 0.377327288 0.16111200
## relief 0.184388894 0.31156652 0.181135500 0.521539337 0.21087122
## happiness 0.180547009 0.46871542 0.247641673 0.355212774 0.29075173
## complexity 0.533139867 0.41333604 0.338608420 0.215797359 -0.03905890
## relevance 0.090415845 -0.15082436 0.172085003 0.465664726 0.60944715
## reliability -0.066927880 -0.02284184 -0.002888018 0.315875981 0.33098109
## relationships 0.395769596 0.68197473 0.408911388 0.110144852 0.08916321
## conflict 0.182168846 0.69676999 0.155388795 0.085332492 0.02860439
## politeness 0.080988041 0.53474953 0.360935953 0.095600389 -0.05349988
## notlying 0.308208591 0.62809134 0.186868746 -0.151453358 -0.21645301
## unsureresp. 1.000000000 0.40472027 0.314664234 -0.004332025 0.01374481
## exclusion 0.404720274 1.00000000 0.254157552 -0.021008841 -0.07125674
## self.percept. 0.314664234 0.25415755 1.000000000 0.262801576 0.07804517
## actiongeneral -0.004332025 -0.02100884 0.262801576 1.000000000 0.41588901
## actionnow 0.013744813 -0.07125674 0.078045168 0.415889008 1.00000000
## disadvantage 0.503223051 0.56858830 0.216124847 0.041625637 0.10398110
## goalinterfer 0.339235347 0.49863882 0.302478780 0.148633438 0.16500747
## socialobligat 0.233362257 0.32287535 0.476300207 0.373983362 0.27700630
## legalobligat 0.463978017 0.41466188 0.424257344 0.125540183 -0.03500317
## surprise 0.213762266 0.50793266 0.177206024 0.031958948 0.03933498
## fairness 0.287480648 0.52284411 0.326774952 -0.005473033 -0.13911626
## disadvantage goalinterfer socialobligat legalobligat surprise
## expectation 0.47136725 0.22447773 0.24102440 0.178082737 0.29827511
## valence 0.44383993 0.38053997 0.27068457 0.306392214 0.37030262
## arousal 0.50364130 0.41384353 0.43721261 0.350891252 0.50720410
## sadness 0.42605258 0.49832350 0.22187142 0.278761688 0.51896736
## anger 0.51994169 0.44654456 0.19203064 0.350448158 0.40035685
## fear 0.39204895 0.41233492 0.34336643 0.254743682 0.42900537
## jealousy 0.42291186 0.39936930 0.28797640 0.286899675 0.43850246
## envy 0.42114370 0.49051589 0.24552471 0.350890771 0.44116045
## disgust 0.36753367 0.36105042 0.30346051 0.413845716 0.55736491
## regret 0.44717641 0.43378830 0.24805682 0.294945343 0.37955473
## feelinggood 0.37547806 0.42124449 0.32461607 0.306622753 0.32350914
## relief 0.23466969 0.32807969 0.27698688 0.205758391 0.27083988
## happiness 0.38150969 0.49096875 0.45355607 0.282479227 0.30114904
## complexity 0.41301743 0.21183473 0.37930592 0.454757879 0.31231416
## relevance 0.09087669 0.05886978 0.16092091 0.012120353 -0.12845012
## reliability 0.04992747 0.08424464 0.06690525 -0.006494485 -0.07931815
## relationships 0.52625146 0.50270858 0.34921242 0.563229393 0.31312312
## conflict 0.43201179 0.37848841 0.26700066 0.210203382 0.44204042
## politeness 0.35480905 0.35159045 0.42690835 0.446093760 0.48318632
## notlying 0.41186255 0.33104194 0.20797168 0.508220119 0.32220368
## unsureresp. 0.50322305 0.33923535 0.23336226 0.463978017 0.21376227
## exclusion 0.56858830 0.49863882 0.32287535 0.414661878 0.50793266
## self.percept. 0.21612485 0.30247878 0.47630021 0.424257344 0.17720602
## actiongeneral 0.04162564 0.14863344 0.37398336 0.125540183 0.03195895
## actionnow 0.10398110 0.16500747 0.27700630 -0.035003171 0.03933498
## disadvantage 1.00000000 0.56443239 0.20416084 0.453749680 0.35878597
## goalinterfer 0.56443239 1.00000000 0.31843970 0.383162967 0.31222504
## socialobligat 0.20416084 0.31843970 1.00000000 0.478746325 0.36801136
## legalobligat 0.45374968 0.38316297 0.47874633 1.000000000 0.33496204
## surprise 0.35878597 0.31222504 0.36801136 0.334962035 1.00000000
## fairness 0.36467448 0.42317115 0.28599406 0.593106498 0.32032763
## fairness
## expectation 0.141614036
## valence 0.346150677
## arousal 0.280786503
## sadness 0.341049388
## anger 0.426794369
## fear 0.267676791
## jealousy 0.295736432
## envy 0.354123665
## disgust 0.471935093
## regret 0.412364121
## feelinggood 0.162116792
## relief 0.048839300
## happiness 0.332705828
## complexity 0.322566855
## relevance -0.118586998
## reliability 0.034561349
## relationships 0.576332216
## conflict 0.392487876
## politeness 0.282995854
## notlying 0.701165087
## unsureresp. 0.287480648
## exclusion 0.522844113
## self.percept. 0.326774952
## actiongeneral -0.005473033
## actionnow -0.139116257
## disadvantage 0.364674478
## goalinterfer 0.423171151
## socialobligat 0.285994058
## legalobligat 0.593106498
## surprise 0.320327633
## fairness 1.000000000
mat_got_noDI <- corr_scenario_noDI$cor_matrix[[which(corr_scenario$Scenario_Content == "ending_GoT")]]
mat_long_got_noDI <- melt(mat_got_noDI, varnames = c("Var1", "Var2"), value.name = "r")
limits <- c(-1, 1)
ggplot(mat_long_got_noDI, aes(x = Var1, y = Var2, fill = r)) +
geom_tile(color = "white") +
geom_text(aes(label = round(r, 2)), size = 3) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = limits) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()
)+
ggtitle("Correlation matrix: ending GoT no DI")
strong_got_noDI <- mat_long_got_noDI %>%
filter(as.numeric(factor(Var1)) < as.numeric(factor(Var2))) %>%
filter(abs(r) > 0.5) %>%
mutate(direction = ifelse(r > 0, "positive", "negative")) %>%
arrange(desc(abs(r)))
Macht aber keinen Sinn weil Zusammenhang niedrige Motive Werte und want to know bin == 0 werden auch mit positiver Korr angezeigt -> nochmal überlegen ### Hiv
dat_hiv <- dat_short %>%
filter(Scenario_Content == "hiv_test")
cor_DI_scenario <- sapply(motive_vars, function(x) {
cor(dat_hiv[[x]], dat_hiv$want_to_know_bin, use = "pairwise.complete.obs")
})
cor_DI_scenario
## expectation valence arousal sadness anger
## 0.051967414 0.031661773 -0.249105568 0.047818491 0.070988602
## fear jealousy envy disgust regret
## 0.033061800 0.131429006 0.047809416 0.083127979 0.083768105
## feelinggood relief happiness complexity relevance
## -0.066836244 0.027426281 0.088958356 0.085994502 -0.108597055
## reliability relationships conflict politeness notlying
## 0.060685796 0.003811452 -0.178637906 0.019642422 0.090584225
## unsureresp. exclusion self.percept. actiongeneral actionnow
## 0.040008581 0.047003563 -0.185068274 0.030700365 0.030580217
## disadvantage goalinterfer socialobligat legalobligat surprise
## 0.056648083 0.050780458 0.027317172 0.070736372 0.018803910
## fairness
## 0.045627212
dat_kardashian <- dat_short %>%
filter(Scenario_Content == "Kardashian_home")
cor_DI_kardashian <- sapply(motive_vars, function(x) {
cor(dat_kardashian[[x]], dat_kardashian$want_to_know_bin, use = "pairwise.complete.obs")
})
cor_DI_kardashian
## expectation valence arousal sadness anger
## -0.149787911 -0.077744947 -0.083134848 -0.097466264 -0.002140862
## fear jealousy envy disgust regret
## -0.078663975 -0.161083044 -0.161061364 -0.068497476 -0.119500724
## feelinggood relief happiness complexity relevance
## -0.163037523 -0.020156523 -0.169415093 -0.113025986 -0.169730656
## reliability relationships conflict politeness notlying
## -0.109653287 -0.048878873 -0.047559520 -0.034338086 0.016146713
## unsureresp. exclusion self.percept. actiongeneral actionnow
## -0.033349563 0.006038017 -0.063315004 -0.080015142 -0.107573680
## disadvantage goalinterfer socialobligat legalobligat surprise
## -0.055367217 -0.075548357 0.041495464 -0.025483300 -0.164946583
## fairness
## 0.027141479
# In ein tibble umwandeln
cor_DI_kardashian_df <- tibble(
rating = names(cor_DI_kardashian),
r = cor_DI_kardashian
)
# Barplot
ggplot(cor_DI_kardashian_df, aes(x = reorder(rating, r), y = r, fill = r)) +
geom_col() +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0, limits = c(-1, 1)) +
coord_flip() + # horizontal für bessere Lesbarkeit
theme_minimal() +
labs(
title = "Punkt-Biserial-Korrelation: Ratings vs. want_to_know_bin",
x = "Rating",
y = "r"
)
dat_short %>%
group_by(want_to_know_bin) %>%
summarise(mean_reliability = mean(reliability, na.rm = TRUE),
sd_reliability = sd(reliability, na.rm = TRUE),
n = n())
## # A tibble: 2 × 4
## want_to_know_bin mean_reliability sd_reliability n
## <dbl> <dbl> <dbl> <int>
## 1 0 1.44 17.8 2283
## 2 1 -5.99 29.4 950
dat_hiv %>%
group_by(want_to_know_bin) %>%
summarise(
across(all_of(motive_vars),
list(mean = ~mean(.x, na.rm = TRUE),
sd = ~sd(.x, na.rm = TRUE)),
.names = "{.col}_{.fn}"),
n = n()
)
## # A tibble: 2 × 64
## want_to_know_bin expectation_mean expectation_sd valence_mean valence_sd
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 -0.673 20.2 1.63 14.5
## 2 1 3.43 2.37 3.43 2.51
## # ℹ 59 more variables: arousal_mean <dbl>, arousal_sd <dbl>,
## # sadness_mean <dbl>, sadness_sd <dbl>, anger_mean <dbl>, anger_sd <dbl>,
## # fear_mean <dbl>, fear_sd <dbl>, jealousy_mean <dbl>, jealousy_sd <dbl>,
## # envy_mean <dbl>, envy_sd <dbl>, disgust_mean <dbl>, disgust_sd <dbl>,
## # regret_mean <dbl>, regret_sd <dbl>, feelinggood_mean <dbl>,
## # feelinggood_sd <dbl>, relief_mean <dbl>, relief_sd <dbl>,
## # happiness_mean <dbl>, happiness_sd <dbl>, complexity_mean <dbl>, …
dat_hiv <- dat_hiv %>%
mutate(across(all_of(motive_vars), ~ ifelse(.x == -99, NA, .x)))
str(dat_hiv[motive_vars])
## tibble [108 × 31] (S3: tbl_df/tbl/data.frame)
## $ expectation : num [1:108] 5 2 6 6 1 4 6 1 4 3 ...
## $ valence : num [1:108] 4 2 6 4 6 6 6 1 5 4 ...
## $ arousal : num [1:108] 5 2 6 5 6 6 6 3 5 1 ...
## $ sadness : num [1:108] 5 1 6 4 6 5 6 1 5 4 ...
## $ anger : num [1:108] 6 3 NA 4 3 5 6 1 4 3 ...
## $ fear : num [1:108] 6 2 6 6 6 6 4 3 5 5 ...
## $ jealousy : num [1:108] 3 3 NA 2 2 1 3 1 4 NA ...
## $ envy : num [1:108] 1 3 NA 2 NA 1 3 1 3 NA ...
## $ disgust : num [1:108] 4 3 2 2 NA 6 3 1 3 1 ...
## $ regret : num [1:108] 2 2 6 3 2 6 1 2 5 5 ...
## $ feelinggood : num [1:108] 4 4 3 2 5 1 1 2 1 4 ...
## $ relief : num [1:108] 5 4 6 5 4 5 1 5 5 5 ...
## $ happiness : num [1:108] 5 4 6 2 4 1 1 1 4 NA ...
## $ complexity : num [1:108] 3 2 NA 2 5 4 1 1 5 NA ...
## $ relevance : num [1:108] 6 6 6 6 6 6 6 5 6 4 ...
## $ reliability : num [1:108] 6 6 NA 6 5 6 1 6 5 NA ...
## $ relationships: num [1:108] 6 2 6 6 6 5 6 4 6 5 ...
## $ conflict : num [1:108] 4 2 3 3 6 5 1 2 4 2 ...
## $ politeness : num [1:108] 4 4 NA 2 NA 1 5 1 4 NA ...
## $ notlying : num [1:108] 3 2 6 4 1 1 1 3 6 NA ...
## $ unsureresp. : num [1:108] 5 2 6 3 4 6 3 2 5 3 ...
## $ exclusion : num [1:108] 5 1 2 5 4 5 3 2 3 NA ...
## $ self.percept.: num [1:108] 6 3 6 5 5 6 6 4 5 5 ...
## $ actiongeneral: num [1:108] 5 5 6 5 6 6 2 3 6 NA ...
## $ actionnow : num [1:108] 6 6 3 6 6 6 6 4 5 NA ...
## $ disadvantage : num [1:108] 5 2 NA 6 3 6 1 2 5 NA ...
## $ goalinterfer : num [1:108] 5 1 NA 6 5 6 5 1 6 4 ...
## $ socialobligat: num [1:108] 6 5 6 6 6 6 6 5 5 4 ...
## $ legalobligat : num [1:108] 5 4 2 5 5 6 6 3 3 4 ...
## $ surprise : num [1:108] 1 3 NA 5 2 1 1 2 2 NA ...
## $ fairness : num [1:108] 3 4 NA 2 5 4 3 1 5 NA ...
Unsicher ob das sinn macht weil Mittelwerte sehr unterschiedlich groß
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
dat_hiv_summary <- dat_hiv %>%
group_by(want_to_know_bin) %>%
summarise(
across(all_of(motive_vars),
list(mean = ~mean(ifelse(.x == -99, NA, .x), na.rm = TRUE),
sd = ~sd(ifelse(.x == -99, NA, .x), na.rm = TRUE)),
.names = "{.col}_{.fn}"),
n = n()
)
dat_hiv_long <- dat_hiv_summary %>%
pivot_longer(
cols = -c(want_to_know_bin, n),
names_to = c("motive", ".value"),
names_sep = "_"
)
dat_hiv_long <- dat_hiv_long %>%
group_by(motive) %>%
mutate(mean_overall = mean(mean, na.rm = TRUE)) %>%
ungroup()
ggplot(dat_hiv_long, aes(x = reorder(motive, mean), y = mean,
fill = as.factor(want_to_know_bin))) +
geom_col(position = "dodge") +
coord_flip() +
theme_minimal() +
labs(
x = "Motive",
y = "Mean rating",
fill = "Want to know (1 = no)"
)